From b76d67e654a806a859c3b5a221992bfdc5cab7b4 Mon Sep 17 00:00:00 2001
From: kharshith-k <kharshith@google.com>
Date: Thu, 26 Jun 2025 05:35:53 +0000
Subject: [PATCH 01/11] Added DLRM.ipynb and generated dlrm.py

---
 examples/keras_rs/dlrm.py          |  406 ++++
 examples/keras_rs/ipynb/DLRM.ipynb | 2978 ++++++++++++++++++++++++++++
 2 files changed, 3384 insertions(+)
 create mode 100644 examples/keras_rs/dlrm.py
 create mode 100644 examples/keras_rs/ipynb/DLRM.ipynb

diff --git a/examples/keras_rs/dlrm.py b/examples/keras_rs/dlrm.py
new file mode 100644
index 0000000000..b634a133c9
--- /dev/null
+++ b/examples/keras_rs/dlrm.py
@@ -0,0 +1,406 @@
+"""
+Title: Ranking with Deep Learning Recommendation Model using Dot Interaction
+Author: Harshith Kulkarni
+Date created: 2025/06/02
+Last modified: 2025/06/20
+Description: Rank movies with DLRM using Dot Interaction
+"""
+
+"""
+## Introduction
+
+This tutorial demonstrates how to use Deep Learning Recommendation Model (DLRM) to
+effectively
+learn relationship between movies and ratings using DotInteraction
+
+The Deep Learning Recommendation Model (DLRM) architecture is designed for more effective
+learning of explicit and bounded-degree features. It comprises three main
+components: an input layer (typically an embedding layer), a dot interaction layer for
+modeling explicit feature interactions, and a deep network for capturing implicit
+interactions.
+
+The dot interaction layer is the core of the DLRM, which computes the pairwise dot
+products between different feature embeddings.
+
+The deep network is a standard feedforward multilayer perceptron
+(MLP). These two networks are then combined to form the DLRM.
+
+Now that we know a little bit about DLRM, let's start writing some code. We will
+first train a DLRM on a real-world dataset, and demonstrate that the model has indeed
+learnt important feature crosses.
+
+Let's set the backend to JAX, and get our imports sorted.
+"""
+
+"""shell
+!pip install  jax_tpu_embedding keras-rs
+"""
+
+import os
+
+os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`
+
+import keras
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+import keras_rs
+
+"""
+Let's also define variables which will be reused throughout the example.
+"""
+
+MOVIELENS_CONFIG = {
+    # features
+    "int_features": [
+        "movie_id",
+        "user_id",
+        "user_gender",
+        "bucketized_user_age",
+    ],
+    "str_features": [
+        "user_zip_code",
+        "user_occupation_text",
+    ],
+    # model
+    "embedding_dim": 8,
+    "deep_net_num_units": [192, 192, 192],
+    # training
+    "learning_rate": 1e-2,
+    "num_epochs": 25,
+    "batch_size": 8192,
+}
+
+
+"""
+Here, we define a helper function for visualising weights of the cross layer in
+order to better understand its functioning. Also, we define a function for
+compiling, training and evaluating a given model.
+"""
+
+
+def plot_training_metrics(history):
+    """Graphs all metrics tracked in the history object."""
+    plt.figure(figsize=(12, 6))
+
+    for metric_name, metric_values in history.history.items():
+        plt.plot(metric_values, label=metric_name.replace("_", " ").title())
+
+    plt.title("Metrics over Epochs")
+    plt.xlabel("Epoch")
+    plt.ylabel("Metric Value")
+    plt.legend()
+    plt.grid(True)
+
+
+def visualize_layer(matrix, features, cmap=plt.cm.Blues):
+    plt.figure(figsize=(9, 9))
+
+    im = plt.matshow(matrix, cmap=cmap)
+
+    ax = plt.gca()
+    divider = make_axes_locatable(plt.gca())
+    cax = divider.append_axes("right", size="5%", pad=0.05)
+    plt.colorbar(im, cax=cax)
+    cax.tick_params(labelsize=10)
+
+    # Set tick locations explicitly before setting labels
+    ax.set_xticks(np.arange(len(features)))
+    ax.set_yticks(np.arange(len(features)))
+
+    ax.set_xticklabels(features, rotation=45, fontsize=5)
+    ax.set_yticklabels(features, fontsize=5)
+
+    plt.show()
+
+
+def train_and_evaluate(
+    learning_rate,
+    epochs,
+    train_data,
+    test_data,
+    model,
+    plot_metrics=False,
+):
+    optimizer = keras.optimizers.AdamW(learning_rate=learning_rate)
+    loss = keras.losses.MeanSquaredError()
+    rmse = keras.metrics.RootMeanSquaredError()
+
+    model.compile(
+        optimizer=optimizer,
+        loss=loss,
+        metrics=[rmse],
+    )
+
+    history = model.fit(
+        train_data,
+        epochs=epochs,
+        verbose=1,
+    )
+    if plot_metrics:
+        plot_training_metrics(history)
+
+    results = model.evaluate(test_data, return_dict=True, verbose=1)
+    rmse_value = results["root_mean_squared_error"]
+
+    return rmse_value, model.count_params()
+
+
+def print_stats(rmse_list, num_params, model_name):
+    # Report metrics.
+    num_trials = len(rmse_list)
+    avg_rmse = np.mean(rmse_list)
+    std_rmse = np.std(rmse_list)
+
+    if num_trials == 1:
+        print(f"{model_name}: RMSE = {avg_rmse}; #params = {num_params}")
+    else:
+        print(f"{model_name}: RMSE = {avg_rmse} ± {std_rmse}; #params = {num_params}")
+
+
+"""
+## Real-world example
+
+Let's use the MovieLens 100K dataset. This dataset is used to train models to
+predict users' movie ratings, based on user-related features and movie-related
+features.
+
+### Preparing the dataset
+
+The dataset processing steps here are similar to what's given in the
+[basic ranking](/keras_rs/examples/basic_ranking/)
+tutorial. Let's load the dataset, and keep only the useful columns.
+"""
+
+ratings_ds = tfds.load("movielens/100k-ratings", split="train")
+ratings_ds = ratings_ds.map(
+    lambda x: (
+        {
+            "movie_id": int(x["movie_id"]),
+            "user_id": int(x["user_id"]),
+            "user_gender": int(x["user_gender"]),
+            "user_zip_code": x["user_zip_code"],
+            "user_occupation_text": x["user_occupation_text"],
+            "bucketized_user_age": int(x["bucketized_user_age"]),
+        },
+        x["user_rating"],  # label
+    )
+)
+
+"""
+For every feature, let's get the list of unique values, i.e., vocabulary, so
+that we can use that for the embedding layer.
+"""
+
+vocabularies = {}
+for feature_name in MOVIELENS_CONFIG["int_features"] + MOVIELENS_CONFIG["str_features"]:
+    vocabulary = ratings_ds.batch(10_000).map(lambda x, y: x[feature_name])
+    vocabularies[feature_name] = np.unique(np.concatenate(list(vocabulary)))
+
+"""
+One thing we need to do is to use `keras.layers.StringLookup` and
+`keras.layers.IntegerLookup` to convert all features into indices, which can
+then be fed into embedding layers.
+"""
+
+lookup_layers = {}
+lookup_layers.update(
+    {
+        feature: keras.layers.IntegerLookup(vocabulary=vocabularies[feature])
+        for feature in MOVIELENS_CONFIG["int_features"]
+    }
+)
+lookup_layers.update(
+    {
+        feature: keras.layers.StringLookup(vocabulary=vocabularies[feature])
+        for feature in MOVIELENS_CONFIG["str_features"]
+    }
+)
+
+ratings_ds = ratings_ds.map(
+    lambda x, y: (
+        {
+            feature_name: lookup_layers[feature_name](x[feature_name])
+            for feature_name in vocabularies
+        },
+        y,
+    )
+)
+
+"""
+Let's split our data into train and test sets. We also use `cache()` and
+`prefetch()` for better performance.
+"""
+
+ratings_ds = ratings_ds.shuffle(100_000)
+
+train_ds = (
+    ratings_ds.take(80_000)
+    .batch(MOVIELENS_CONFIG["batch_size"])
+    .cache()
+    .prefetch(tf.data.AUTOTUNE)
+)
+test_ds = (
+    ratings_ds.skip(80_000)
+    .batch(MOVIELENS_CONFIG["batch_size"])
+    .take(20_000)
+    .cache()
+    .prefetch(tf.data.AUTOTUNE)
+)
+
+"""
+### Building the model
+
+The model will have embedding layers, followed by DotInteraction and feedforward
+layers.
+"""
+
+
+class DLRM(keras.Model):
+    def __init__(
+        self,
+        dense_num_units_lst,
+        embedding_dim=MOVIELENS_CONFIG["embedding_dim"],
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # Layers.
+
+        self.embedding_layers = []
+        for feature_name, vocabulary in vocabularies.items():
+            self.embedding_layers.append(
+                keras.layers.Embedding(
+                    input_dim=len(vocabulary) + 1,
+                    output_dim=embedding_dim,
+                )
+            )
+
+        self.dot_layer = keras_rs.layers.DotInteraction()
+
+        self.dense_layers = []
+        for num_units in dense_num_units_lst:
+            self.dense_layers.append(keras.layers.Dense(num_units, activation="relu"))
+
+        self.output_layer = keras.layers.Dense(1)
+
+        # Attributes.
+        self.dense_num_units_lst = dense_num_units_lst
+        self.embedding_dim = embedding_dim
+
+    def call(self, inputs):
+        embeddings = []
+        for feature_name, embedding_layer in zip(vocabularies, self.embedding_layers):
+            embeddings.append(embedding_layer(inputs[feature_name]))
+
+        # Pass the list of embeddings to the DotInteraction layer
+        x = self.dot_layer(embeddings)
+
+        for dense_layer in self.dense_layers:
+            x = dense_layer(x)
+
+        x = self.output_layer(x)
+
+        return x
+
+
+dot_network = DLRM(
+    dense_num_units_lst=MOVIELENS_CONFIG["deep_net_num_units"],
+    embedding_dim=MOVIELENS_CONFIG["embedding_dim"],
+)
+rmse, dot_network_num_params = train_and_evaluate(
+    learning_rate=MOVIELENS_CONFIG["learning_rate"],
+    epochs=MOVIELENS_CONFIG["num_epochs"],
+    train_data=train_ds,
+    test_data=test_ds,
+    model=dot_network,
+    plot_metrics=True,
+)
+print_stats(
+    rmse_list=[rmse],
+    num_params=dot_network_num_params,
+    model_name="Dot Network",
+)
+
+
+"""
+### Visualizing feature interactions
+
+The DotInteraction layer itself doesn't have a conventional "weight" matrix like a Dense
+layer. Instead, its function is to compute the dot product between the embedding vectors
+of your features.
+
+To visualize the strength of these interactions, we can calculate a matrix representing
+the pairwise interaction strength between all feature embeddings. A common way to do this
+is to take the dot product of the embedding matrices for each pair of features and then
+aggregate the result into a single value (like the mean of the absolute values) that
+represents the overall interaction strength.
+"""
+
+
+def get_dot_interaction_matrix(model, feature_names):
+    # Extract the trained embedding weights from the model
+    embedding_weights = [layer.get_weights()[0] for layer in model.embedding_layers]
+
+    num_features = len(feature_names)
+    interaction_matrix = np.zeros((num_features, num_features))
+
+    # Iterate through each pair of features to calculate their interaction strength
+    for i in range(num_features):
+        for j in range(num_features):
+            # Calculate the dot product between the two embedding matrices
+            interaction = np.dot(embedding_weights[i], embedding_weights[j].T)
+            # Take the mean of the absolute values as a measure of interaction strength
+            interaction_strength = np.mean(np.abs(interaction))
+            interaction_matrix[i, j] = interaction_strength
+
+    return interaction_matrix
+
+
+# Get the list of feature names in the correct order
+feature_names = list(vocabularies.keys())
+
+# Calculate the interaction matrix
+interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)
+
+# Visualize the matrix as a heatmap
+print("\nVisualizing the feature interaction strengths:")
+visualize_layer(interaction_matrix, feature_names)
+
+dlrm_rmse_list = []
+
+for _ in range(20):
+
+    dot_network = DLRM(
+        dense_num_units_lst=MOVIELENS_CONFIG["deep_net_num_units"],
+        embedding_dim=MOVIELENS_CONFIG["embedding_dim"],
+    )
+    rmse, dot_network_num_params = train_and_evaluate(
+        learning_rate=MOVIELENS_CONFIG["learning_rate"],
+        epochs=MOVIELENS_CONFIG["num_epochs"],
+        train_data=train_ds,
+        test_data=test_ds,
+        model=dot_network,
+    )
+    dlrm_rmse_list.append(rmse)
+
+print_stats(
+    rmse_list=dlrm_rmse_list,
+    num_params=dot_network_num_params,
+    model_name="Dot Network",
+)
+
+
+def plot_rmse(rmse_list, model_name):
+    plt.figure()
+    plt.plot(rmse_list)
+    plt.title(f"RMSE over trials for {model_name}")
+    plt.xlabel("Trial")
+    plt.ylabel("RMSE")
+    plt.show()
+
+
+plot_rmse(dlrm_rmse_list, "Dot Network")
diff --git a/examples/keras_rs/ipynb/DLRM.ipynb b/examples/keras_rs/ipynb/DLRM.ipynb
new file mode 100644
index 0000000000..6b1f7c471a
--- /dev/null
+++ b/examples/keras_rs/ipynb/DLRM.ipynb
@@ -0,0 +1,2978 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33c74727"
+      },
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This tutorial demonstrates how to use Deep Learning Recommendation Model (DLRM) to effectively\n",
+        "learn relationship between movies and ratings using DotInteraction\n",
+        "\n",
+        "The Deep Learning Recommendation Model (DLRM) architecture is designed for more effective\n",
+        "learning of explicit and bounded-degree features. It comprises three main\n",
+        "components: an input layer (typically an embedding layer), a dot interaction layer for\n",
+        "modeling explicit feature interactions, and a deep network for capturing implicit interactions.\n",
+        "\n",
+        "The dot interaction layer is the core of the DLRM, which computes the pairwise dot products between different feature embeddings.\n",
+        "\n",
+        "The deep network is a standard feedforward multilayer perceptron\n",
+        "(MLP). These two networks are then combined to form the DLRM.\n",
+        "\n",
+        "Now that we know a little bit about DLRM, let's start writing some code. We will\n",
+        "first train a DLRM on a real-world dataset, and demonstrate that the model has indeed\n",
+        "learnt important feature crosses.\n",
+        "\n",
+        "Let's set the backend to JAX, and get our imports sorted."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "blj4_0Wg62kR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting jax_tpu_embedding\n",
+            "  Downloading jax_tpu_embedding-0.1.0.dev20250618-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n",
+            "Collecting keras-rs\n",
+            "  Downloading keras_rs-0.2.1-py3-none-any.whl.metadata (5.2 kB)\n",
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+            "Downloading jax_tpu_embedding-0.1.0.dev20250618-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading keras_rs-0.2.1-py3-none-any.whl (90 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.3/90.3 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: keras-rs, jax_tpu_embedding\n",
+            "Successfully installed jax_tpu_embedding-0.1.0.dev20250618 keras-rs-0.2.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install  jax_tpu_embedding keras-rs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QeFXrN1962kT"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # `\"tensorflow\"`/`\"torch\"`\n",
+        "\n",
+        "import keras\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "import tensorflow_datasets as tfds\n",
+        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+        "\n",
+        "import keras_rs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gHdT0D1762kT"
+      },
+      "source": [
+        "Let's also define variables which will be reused throughout the example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GRJOAiMv62kT"
+      },
+      "outputs": [],
+      "source": [
+        "MOVIELENS_CONFIG = {\n",
+        "    # features\n",
+        "    \"int_features\": [\n",
+        "        \"movie_id\",\n",
+        "        \"user_id\",\n",
+        "        \"user_gender\",\n",
+        "        \"bucketized_user_age\",\n",
+        "    ],\n",
+        "    \"str_features\": [\n",
+        "        \"user_zip_code\",\n",
+        "        \"user_occupation_text\",\n",
+        "    ],\n",
+        "    # model\n",
+        "    \"embedding_dim\": 8,\n",
+        "    \"deep_net_num_units\": [192, 192, 192],\n",
+        "    # training\n",
+        "    \"learning_rate\": 1e-2,\n",
+        "    \"num_epochs\": 25,\n",
+        "    \"batch_size\": 8192,\n",
+        "}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_6J6XFNC62kT"
+      },
+      "source": [
+        "Here, we define a helper function for visualising weights of the cross layer in\n",
+        "order to better understand its functioning. Also, we define a function for\n",
+        "compiling, training and evaluating a given model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jyojSTKw62kT"
+      },
+      "outputs": [],
+      "source": [
+        "def plot_training_metrics(history):\n",
+        "    \"\"\"Graphs all metrics tracked in the history object.\"\"\"\n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "\n",
+        "    for metric_name, metric_values in history.history.items():\n",
+        "        plt.plot(metric_values, label=metric_name.replace('_', ' ').title())\n",
+        "\n",
+        "    plt.title('Metrics over Epochs')\n",
+        "    plt.xlabel('Epoch')\n",
+        "    plt.ylabel('Metric Value')\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "\n",
+        "def visualize_layer(matrix, features, cmap=plt.cm.Blues):\n",
+        "    plt.figure(figsize=(9, 9))\n",
+        "\n",
+        "    im = plt.matshow(matrix, cmap=cmap)\n",
+        "\n",
+        "    ax = plt.gca()\n",
+        "    divider = make_axes_locatable(plt.gca())\n",
+        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "    plt.colorbar(im, cax=cax)\n",
+        "    cax.tick_params(labelsize=10)\n",
+        "\n",
+        "    # Set tick locations explicitly before setting labels\n",
+        "    ax.set_xticks(np.arange(len(features)))\n",
+        "    ax.set_yticks(np.arange(len(features)))\n",
+        "\n",
+        "    ax.set_xticklabels(features, rotation=45, fontsize=5)\n",
+        "    ax.set_yticklabels(features, fontsize=5)\n",
+        "\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "def train_and_evaluate(\n",
+        "    learning_rate,\n",
+        "    epochs,\n",
+        "    train_data,\n",
+        "    test_data,\n",
+        "    model,\n",
+        "    plot_metrics=False,\n",
+        "):\n",
+        "    optimizer = keras.optimizers.AdamW(learning_rate=learning_rate)\n",
+        "    loss = keras.losses.MeanSquaredError()\n",
+        "    rmse = keras.metrics.RootMeanSquaredError()\n",
+        "\n",
+        "    model.compile(\n",
+        "        optimizer=optimizer,\n",
+        "        loss=loss,\n",
+        "        metrics=[rmse],\n",
+        "    )\n",
+        "\n",
+        "    history = model.fit(\n",
+        "        train_data,\n",
+        "        epochs=epochs,\n",
+        "        verbose=1,\n",
+        "    )\n",
+        "    if plot_metrics:\n",
+        "      plot_training_metrics(history)\n",
+        "\n",
+        "    results = model.evaluate(test_data, return_dict=True, verbose=1)\n",
+        "    rmse_value = results[\"root_mean_squared_error\"]\n",
+        "\n",
+        "    return rmse_value, model.count_params()\n",
+        "\n",
+        "\n",
+        "def print_stats(rmse_list, num_params, model_name):\n",
+        "    # Report metrics.\n",
+        "    num_trials = len(rmse_list)\n",
+        "    avg_rmse = np.mean(rmse_list)\n",
+        "    std_rmse = np.std(rmse_list)\n",
+        "\n",
+        "    if num_trials == 1:\n",
+        "        print(f\"{model_name}: RMSE = {avg_rmse}; #params = {num_params}\")\n",
+        "    else:\n",
+        "        print(f\"{model_name}: RMSE = {avg_rmse} ± {std_rmse}; #params = {num_params}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tVHJBIJ_62kV"
+      },
+      "source": [
+        "## Real-world example\n",
+        "\n",
+        "Let's use the MovieLens 100K dataset. This dataset is used to train models to\n",
+        "predict users' movie ratings, based on user-related features and movie-related\n",
+        "features.\n",
+        "\n",
+        "### Preparing the dataset\n",
+        "\n",
+        "The dataset processing steps here are similar to what's given in the\n",
+        "[basic ranking](/keras_rs/examples/basic_ranking/)\n",
+        "tutorial. Let's load the dataset, and keep only the useful columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kipGDLuZ62kW",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187,
+          "referenced_widgets": [
+            "58dfd53b556843dabaecd6cf2d6935ef",
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+            "b1fe6058815a412d968e69beea11ecb5",
+            "06299c873ad44899a078291779f6e8e4",
+            "ac6c0a7f4a574b2880c6e7a76c54f82b",
+            "2fd3f84a72f04a809f68af13b7430895",
+            "749464bb77044f2ba799e1c3595d7b37",
+            "0d6cb36062bc48f792c40439ca1d1cee",
+            "d04e7b6888de4ca6b5eb77145d5efdde",
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+            "a5abded0002a4afab3e20a89fe56c893",
+            "dc7d6718b55b499ebde1bd8c3b77c108",
+            "799bd47f82f2439ab87a96c9ceabc246",
+            "069c8512afb24fb9ba180bd7d5f86122",
+            "d9fc394eeabe49b79910eec9d9b93ceb",
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+            "bba0acfe88c640188a2b45eb174962a6",
+            "9dfa77aae53d46b0a1ad0028a2c87c5e",
+            "8eb493a9d7fe42249acbad63ce15ab0f",
+            "2a7644ab2b7a45b5be4bb8980192be7f",
+            "bb222800db944751a360d3da73211a60",
+            "5a498fb2073245ef8b04911efc6d2d81",
+            "024cbbb854c943319b9be0d743e09f84",
+            "d76021431ab94848ab28d21553c37237",
+            "5ec7292979364685a75a01df20852dee",
+            "c51f2c0b4b684266881cc78606f91c48",
+            "129109799f124e80bf5c36fcb74fbc8d",
+            "874e6e7899994faab2341102a9f407de",
+            "3c94a1d214364e1b9fd7c3ff52a03026",
+            "5643b587cb634078bcf7b3cc637c23e0",
+            "0d1862d8e67e45889c7fde05787d409e",
+            "92d2807a12cb4a9182c70e061bc3598c",
+            "a062a4d30c1147feb8faf96fba1af0df",
+            "6c26c23d5e9544e8b114c816ad667320",
+            "4643620958e34bf685682c3f44780370",
+            "d86b9434231b4cd28ce1611e778454c2",
+            "d5804d8334c54434a447f19188ee751f",
+            "a1fefc93f4ca4fc9866774e28308d62d",
+            "1115049423874cd78f3b85184f8d7e15",
+            "7986925eb5624324aee93da08a630099",
+            "2e706f8ef4d34b39b092199c17262dd7",
+            "a6000ca73675474ea7c2b1b7e6c4186c",
+            "25df22d29aab44578894f0aa0ce53aa4",
+            "6898fd5bc3634fe681a96bd01b629b53",
+            "ec2a121d9157485d9002795e0c86d471",
+            "6a0a7673658a4d77921bd05493ad50b6",
+            "f0b8e85a1e854efa8a43b9ada4109915",
+            "68bb8313572f4baf853e4313627cab07",
+            "71f75584dc4041a0adc23a87dbedc2c2",
+            "12605d31b53048008fabf7e93ebab4c5",
+            "f4a0ae32f65a45e4b2015e164c7963be",
+            "cf2d1c56ac624e7b865769436a3e1f33",
+            "4c3c33ad6f484d30b091858c13ded0da",
+            "78cdea4a88414d109466b4b1ad60ad18",
+            "11ac95d724414a02aec0f5424b271b41"
+          ]
+        },
+        "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:Variant folder /root/tensorflow_datasets/movielens/100k-ratings/0.1.1 has no dataset_info.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Dl Completed...: 0 url [00:00, ? url/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "58dfd53b556843dabaecd6cf2d6935ef"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f3c1148efd194daeae69b8dbcc24c9d5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Extraction completed...: 0 file [00:00, ? file/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dc7d6718b55b499ebde1bd8c3b77c108"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "024cbbb854c943319b9be0d743e09f84"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train examples...: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6c26c23d5e9544e8b114c816ad667320"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ec2a121d9157485d9002795e0c86d471"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1. Subsequent calls will reuse this data.\n"
+          ]
+        }
+      ],
+      "source": [
+        "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
+        "ratings_ds = ratings_ds.map(\n",
+        "    lambda x: (\n",
+        "        {\n",
+        "            \"movie_id\": int(x[\"movie_id\"]),\n",
+        "            \"user_id\": int(x[\"user_id\"]),\n",
+        "            \"user_gender\": int(x[\"user_gender\"]),\n",
+        "            \"user_zip_code\": x[\"user_zip_code\"],\n",
+        "            \"user_occupation_text\": x[\"user_occupation_text\"],\n",
+        "            \"bucketized_user_age\": int(x[\"bucketized_user_age\"]),\n",
+        "        },\n",
+        "        x[\"user_rating\"],  # label\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3DrNFuZi62kW"
+      },
+      "source": [
+        "For every feature, let's get the list of unique values, i.e., vocabulary, so\n",
+        "that we can use that for the embedding layer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pczAh0MV62kW"
+      },
+      "outputs": [],
+      "source": [
+        "vocabularies = {}\n",
+        "for feature_name in MOVIELENS_CONFIG[\"int_features\"] + MOVIELENS_CONFIG[\"str_features\"]:\n",
+        "    vocabulary = ratings_ds.batch(10_000).map(lambda x, y: x[feature_name])\n",
+        "    vocabularies[feature_name] = np.unique(np.concatenate(list(vocabulary)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1PLhYqF062kW"
+      },
+      "source": [
+        "One thing we need to do is to use `keras.layers.StringLookup` and\n",
+        "`keras.layers.IntegerLookup` to convert all features into indices, which can\n",
+        "then be fed into embedding layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pLMvMAIF62kW"
+      },
+      "outputs": [],
+      "source": [
+        "lookup_layers = {}\n",
+        "lookup_layers.update(\n",
+        "    {\n",
+        "        feature: keras.layers.IntegerLookup(vocabulary=vocabularies[feature])\n",
+        "        for feature in MOVIELENS_CONFIG[\"int_features\"]\n",
+        "    }\n",
+        ")\n",
+        "lookup_layers.update(\n",
+        "    {\n",
+        "        feature: keras.layers.StringLookup(vocabulary=vocabularies[feature])\n",
+        "        for feature in MOVIELENS_CONFIG[\"str_features\"]\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "ratings_ds = ratings_ds.map(\n",
+        "    lambda x, y: (\n",
+        "        {\n",
+        "            feature_name: lookup_layers[feature_name](x[feature_name])\n",
+        "            for feature_name in vocabularies\n",
+        "        },\n",
+        "        y,\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XBKk-mX962kW"
+      },
+      "source": [
+        "Let's split our data into train and test sets. We also use `cache()` and\n",
+        "`prefetch()` for better performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ha3v4rHN62kW"
+      },
+      "outputs": [],
+      "source": [
+        "ratings_ds = ratings_ds.shuffle(100_000)\n",
+        "\n",
+        "train_ds = (\n",
+        "    ratings_ds.take(80_000)\n",
+        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
+        "    .cache()\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")\n",
+        "test_ds = (\n",
+        "    ratings_ds.skip(80_000)\n",
+        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
+        "    .take(20_000)\n",
+        "    .cache()\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RXuXvsl062kW"
+      },
+      "source": [
+        "### Building the model\n",
+        "\n",
+        "The model will have embedding layers, followed by DotInteraction and feedforward\n",
+        "layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ww3ZJ6R062kW"
+      },
+      "outputs": [],
+      "source": [
+        "class DLRM(keras.Model):\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        dense_num_units_lst,\n",
+        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        "        **kwargs,\n",
+        "    ):\n",
+        "        super().__init__(**kwargs)\n",
+        "\n",
+        "        # Layers.\n",
+        "\n",
+        "        self.embedding_layers = []\n",
+        "        for feature_name, vocabulary in vocabularies.items():\n",
+        "            self.embedding_layers.append(\n",
+        "                keras.layers.Embedding(\n",
+        "                    input_dim=len(vocabulary) + 1,\n",
+        "                    output_dim=embedding_dim,\n",
+        "                )\n",
+        "            )\n",
+        "\n",
+        "        self.dot_layer = keras_rs.layers.DotInteraction()\n",
+        "\n",
+        "        self.dense_layers = []\n",
+        "        for num_units in dense_num_units_lst:\n",
+        "            self.dense_layers.append(keras.layers.Dense(num_units, activation=\"relu\"))\n",
+        "\n",
+        "        self.output_layer = keras.layers.Dense(1)\n",
+        "\n",
+        "        # Attributes.\n",
+        "        self.dense_num_units_lst = dense_num_units_lst\n",
+        "        self.embedding_dim = embedding_dim\n",
+        "\n",
+        "    def call(self, inputs):\n",
+        "        embeddings = []\n",
+        "        for feature_name, embedding_layer in zip(vocabularies, self.embedding_layers):\n",
+        "            embeddings.append(embedding_layer(inputs[feature_name]))\n",
+        "\n",
+        "        # Pass the list of embeddings to the DotInteraction layer\n",
+        "        x = self.dot_layer(embeddings)\n",
+        "\n",
+        "        for dense_layer in self.dense_layers:\n",
+        "            x = dense_layer(x)\n",
+        "\n",
+        "        x = self.output_layer(x)\n",
+        "\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dot_network = DLRM(\n",
+        "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
+        "    embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        ")\n",
+        "rmse, dot_network_num_params = train_and_evaluate(\n",
+        "    learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
+        "    epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
+        "    train_data=train_ds,\n",
+        "    test_data=test_ds,\n",
+        "    model= dot_network,\n",
+        "    plot_metrics=True\n",
+        ")\n",
+        "print_stats(\n",
+        "    rmse_list=[rmse],\n",
+        "    num_params=dot_network_num_params,\n",
+        "    model_name=\"Dot Network\",\n",
+        ")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "OAoTyyzi_Nau",
+        "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - loss: 8.1493 - root_mean_squared_error: 2.8203\n",
+            "Epoch 2/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 2.8909 - root_mean_squared_error: 1.6905\n",
+            "Epoch 3/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5062 - root_mean_squared_error: 1.2266\n",
+            "Epoch 4/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2285 - root_mean_squared_error: 1.1076\n",
+            "Epoch 5/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.9398 - root_mean_squared_error: 0.9694 \n",
+            "Epoch 6/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.8562 - root_mean_squared_error: 0.9253\n",
+            "Epoch 7/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.7850 - root_mean_squared_error: 0.8860\n",
+            "Epoch 8/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7260 - root_mean_squared_error: 0.8520 \n",
+            "Epoch 9/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6800 - root_mean_squared_error: 0.8246 \n",
+            "Epoch 10/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.6444 - root_mean_squared_error: 0.8028\n",
+            "Epoch 11/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6183 - root_mean_squared_error: 0.7863 \n",
+            "Epoch 12/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5971 - root_mean_squared_error: 0.7727\n",
+            "Epoch 13/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5808 - root_mean_squared_error: 0.7621\n",
+            "Epoch 14/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5676 - root_mean_squared_error: 0.7534 \n",
+            "Epoch 15/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5566 - root_mean_squared_error: 0.7461\n",
+            "Epoch 16/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5474 - root_mean_squared_error: 0.7399\n",
+            "Epoch 17/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5399 - root_mean_squared_error: 0.7347 \n",
+            "Epoch 18/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5333 - root_mean_squared_error: 0.7303\n",
+            "Epoch 19/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5277 - root_mean_squared_error: 0.7264\n",
+            "Epoch 20/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5233 - root_mean_squared_error: 0.7234 \n",
+            "Epoch 21/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5191 - root_mean_squared_error: 0.7205 \n",
+            "Epoch 22/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5156 - root_mean_squared_error: 0.7180\n",
+            "Epoch 23/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5124 - root_mean_squared_error: 0.7158\n",
+            "Epoch 24/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5097 - root_mean_squared_error: 0.7139\n",
+            "Epoch 25/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5069 - root_mean_squared_error: 0.7120 \n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 513ms/step - loss: 0.6062 - root_mean_squared_error: 0.7786\n",
+            "Dot Network: RMSE = 0.7789321541786194; #params = 105025\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E68GrcDN62kW"
+      },
+      "source": [
+        "### Visualizing feature interactions\n",
+        "\n",
+        "The DotInteraction layer itself doesn't have a conventional \"weight\" matrix like a Dense layer. Instead, its function is to compute the dot product between the embedding vectors of your features.\n",
+        "\n",
+        "To visualize the strength of these interactions, we can calculate a matrix representing the pairwise interaction strength between all feature embeddings. A common way to do this is to take the dot product of the embedding matrices for each pair of features and then aggregate the result into a single value (like the mean of the absolute values) that represents the overall interaction strength."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_dot_interaction_matrix(model, feature_names):\n",
+        "    # Extract the trained embedding weights from the model\n",
+        "    embedding_weights = [layer.get_weights()[0] for layer in model.embedding_layers]\n",
+        "\n",
+        "    num_features = len(feature_names)\n",
+        "    interaction_matrix = np.zeros((num_features, num_features))\n",
+        "\n",
+        "    # Iterate through each pair of features to calculate their interaction strength\n",
+        "    for i in range(num_features):\n",
+        "        for j in range(num_features):\n",
+        "            # Calculate the dot product between the two embedding matrices\n",
+        "            interaction = np.dot(embedding_weights[i], embedding_weights[j].T)\n",
+        "            # Take the mean of the absolute values as a measure of interaction strength\n",
+        "            interaction_strength = np.mean(np.abs(interaction))\n",
+        "            interaction_matrix[i, j] = interaction_strength\n",
+        "\n",
+        "    return interaction_matrix"
+      ],
+      "metadata": {
+        "id": "R8WPVVn-Jq81"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get the list of feature names in the correct order\n",
+        "feature_names = list(vocabularies.keys())\n",
+        "\n",
+        "# Calculate the interaction matrix\n",
+        "interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)\n",
+        "\n",
+        "# Visualize the matrix as a heatmap\n",
+        "print(\"\\nVisualizing the feature interaction strengths:\")\n",
+        "visualize_layer(interaction_matrix, feature_names)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 512
+        },
+        "id": "NH-VtGrMJtAt",
+        "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Visualizing the feature interaction strengths:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x900 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 480x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IbpjtvnQ62kW"
+      },
+      "outputs": [],
+      "source": [
+        "dlrm_rmse_list = []\n",
+        "\n",
+        "for _ in range(20):\n",
+        "\n",
+        "    dot_network = DLRM(\n",
+        "        dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
+        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        "    )\n",
+        "    rmse, dot_network_num_params = train_and_evaluate(\n",
+        "        learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
+        "        epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
+        "        train_data=train_ds,\n",
+        "        test_data=test_ds,\n",
+        "        model= dot_network,\n",
+        "    )\n",
+        "    dlrm_rmse_list.append(rmse)\n",
+        "\n",
+        "print_stats(\n",
+        "    rmse_list=dlrm_rmse_list,\n",
+        "    num_params=dot_network_num_params,\n",
+        "    model_name=\"Dot Network\",\n",
+        ")\n",
+        "\n"
+      ]
+    },
+    {
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From 421c24f3a332ad11b69839b7a2230104d5c3fc9f Mon Sep 17 00:00:00 2001
From: kharshith-k <kharshith@google.com>
Date: Thu, 10 Jul 2025 13:09:52 +0530
Subject: [PATCH 02/11] Update dlrm.py

Updated docstrings to match the context according to code
---
 examples/keras_rs/dlrm.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/examples/keras_rs/dlrm.py b/examples/keras_rs/dlrm.py
index b634a133c9..1808875612 100644
--- a/examples/keras_rs/dlrm.py
+++ b/examples/keras_rs/dlrm.py
@@ -1,9 +1,9 @@
 """
-Title: Ranking with Deep Learning Recommendation Model using Dot Interaction
+Title: Ranking with Deep Learning Recommendation Model
 Author: Harshith Kulkarni
 Date created: 2025/06/02
 Last modified: 2025/06/20
-Description: Rank movies with DLRM using Dot Interaction
+Description: Rank movies with DLRM with KerasRS
 """
 
 """
@@ -14,10 +14,10 @@
 learn relationship between movies and ratings using DotInteraction
 
 The Deep Learning Recommendation Model (DLRM) architecture is designed for more effective
-learning of explicit and bounded-degree features. It comprises three main
-components: an input layer (typically an embedding layer), a dot interaction layer for
-modeling explicit feature interactions, and a deep network for capturing implicit
-interactions.
+learning of explicit and bounded-degree features which performs well on both categrical and 
+continuous (sparse/dense) features. It comprises of three main components: input layers 
+(typically an embedding layer for each feature), a dot interaction layer for modeling explicit 
+feature interactions, and MLP based deep network for capturing implicit interactions.
 
 The dot interaction layer is the core of the DLRM, which computes the pairwise dot
 products between different feature embeddings.
@@ -331,7 +331,7 @@ def call(self, inputs):
 
 The DotInteraction layer itself doesn't have a conventional "weight" matrix like a Dense
 layer. Instead, its function is to compute the dot product between the embedding vectors
-of your features.
+of features.
 
 To visualize the strength of these interactions, we can calculate a matrix representing
 the pairwise interaction strength between all feature embeddings. A common way to do this

From 768fb2e0b214baf07e3fde4edb2bcf987cfd1647 Mon Sep 17 00:00:00 2001
From: Harshith K <kharshith@google.com>
Date: Mon, 14 Jul 2025 11:48:56 +0530
Subject: [PATCH 03/11] Modified DLRM.ipynb to add architecture diagram

---
 .DS_Store                                     |  Bin 0 -> 6148 bytes
 examples/.DS_Store                            |  Bin 0 -> 6148 bytes
 examples/keras_rs/.DS_Store                   |  Bin 0 -> 6148 bytes
 examples/keras_rs/dlrm.py                     |    3 +-
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 .../keras_rs/img/dlrm/dlrm_architecture.gif   |  Bin 0 -> 3236181 bytes
 examples/keras_rs/ipynb/DLRM.ipynb            | 1521 ++++++++---------
 7 files changed, 758 insertions(+), 766 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 examples/.DS_Store
 create mode 100644 examples/keras_rs/.DS_Store
 create mode 100644 examples/keras_rs/img/.DS_Store
 create mode 100644 examples/keras_rs/img/dlrm/dlrm_architecture.gif

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 ## Introduction
 
 This tutorial demonstrates how to use Deep Learning Recommendation Model (DLRM) to
-effectively
-learn relationship between movies and ratings using DotInteraction
+effectively learn relationship between movies and ratings using DotInteraction
 
 The Deep Learning Recommendation Model (DLRM) architecture is designed for more effective
 learning of explicit and bounded-degree features which performs well on both categrical and 
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HcmV?d00001

diff --git a/examples/keras_rs/ipynb/DLRM.ipynb b/examples/keras_rs/ipynb/DLRM.ipynb
index 6b1f7c471a..b7b7dfacb1 100644
--- a/examples/keras_rs/ipynb/DLRM.ipynb
+++ b/examples/keras_rs/ipynb/DLRM.ipynb
@@ -9,18 +9,21 @@
         "## Introduction\n",
         "\n",
         "This tutorial demonstrates how to use Deep Learning Recommendation Model (DLRM) to effectively\n",
-        "learn relationship between movies and ratings using DotInteraction\n",
+        "learn relationship between movies and ratings using DotInteraction. For more details, please refer to the [DLRM](https://arxiv.org/pdf/1906.00091) paper.\n",
         "\n",
         "The Deep Learning Recommendation Model (DLRM) architecture is designed for more effective\n",
-        "learning of explicit and bounded-degree features. It comprises three main\n",
-        "components: an input layer (typically an embedding layer), a dot interaction layer for\n",
-        "modeling explicit feature interactions, and a deep network for capturing implicit interactions.\n",
+        "learning of explicit and bounded-degree features which performs well on both categrical and \n",
+        "continuous (sparse/dense) features. It comprises of three main components: input layers \n",
+        "(typically an embedding layer for each feature), a dot interaction layer for modeling explicit \n",
+        "feature interactions, and MLP based deep network for capturing implicit interactions.\n",
         "\n",
         "The dot interaction layer is the core of the DLRM, which computes the pairwise dot products between different feature embeddings.\n",
         "\n",
         "The deep network is a standard feedforward multilayer perceptron\n",
         "(MLP). These two networks are then combined to form the DLRM.\n",
         "\n",
+        "![DLRM Architecture](img/dlrm/dlrm_architecture.gif)\n",
+        "\n",
         "Now that we know a little bit about DLRM, let's start writing some code. We will\n",
         "first train a DLRM on a real-world dataset, and demonstrate that the model has indeed\n",
         "learnt important feature crosses.\n",
@@ -30,80 +33,69 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
-        "id": "blj4_0Wg62kR",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "blj4_0Wg62kR",
         "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Collecting jax_tpu_embedding\n",
-            "  Downloading jax_tpu_embedding-0.1.0.dev20250618-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n",
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-            "Downloading jax_tpu_embedding-0.1.0.dev20250618-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.6 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading keras_rs-0.2.1-py3-none-any.whl (90 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.3/90.3 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hInstalling collected packages: keras-rs, jax_tpu_embedding\n",
-            "Successfully installed jax_tpu_embedding-0.1.0.dev20250618 keras-rs-0.2.1\n"
+            "Requirement already satisfied: keras-rs in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (0.2.1)\n",
+            "Requirement already satisfied: keras in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (3.10.0)\n",
+            "Requirement already satisfied: ml-dtypes in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (0.5.1)\n",
+            "Requirement already satisfied: absl-py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.3.1)\n",
+            "Requirement already satisfied: numpy in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.1.3)\n",
+            "Requirement already satisfied: rich in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (14.0.0)\n",
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+            "Requirement already satisfied: packaging in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (25.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from optree->keras->keras-rs) (4.14.1)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (2.19.2)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->keras->keras-rs) (0.1.2)\n"
           ]
         }
       ],
       "source": [
-        "!pip install  jax_tpu_embedding keras-rs"
+        "!pip install keras-rs"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
         "id": "QeFXrN1962kT"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "ename": "ModuleNotFoundError",
+          "evalue": "No module named 'jax'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m      3\u001b[39m os.environ[\u001b[33m\"\u001b[39m\u001b[33mKERAS_BACKEND\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# `\"tensorflow\"`/`\"torch\"`\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _tf_keras \u001b[38;5;28;01mas\u001b[39;00m _tf_keras\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_tf_keras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m callbacks \u001b[38;5;28;01mas\u001b[39;00m callbacks\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/activations/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;28;01mas\u001b[39;00m deserialize\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get \u001b[38;5;28;01mas\u001b[39;00m get\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m serialize \u001b[38;5;28;01mas\u001b[39;00m serialize\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtypes\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m celu\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m exponential\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/activations.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi_export\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/__init__.py:41\u001b[39m\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtensorflow\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     42\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mtorch\u001b[39m\u001b[33m\"\u001b[39m:\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m distribution_lib\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m image\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/core.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexperimental\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax_sparse\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjnp\u001b[39;00m\n",
+            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'jax'"
+          ]
+        }
+      ],
       "source": [
         "import os\n",
         "\n",
@@ -278,7 +270,6 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "kipGDLuZ62kW",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 187,
@@ -351,110 +342,111 @@
             "11ac95d724414a02aec0f5424b271b41"
           ]
         },
+        "id": "kipGDLuZ62kW",
         "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "WARNING:absl:Variant folder /root/tensorflow_datasets/movielens/100k-ratings/0.1.1 has no dataset_info.json\n"
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Dl Completed...: 0 url [00:00, ? url/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "58dfd53b556843dabaecd6cf2d6935ef",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "58dfd53b556843dabaecd6cf2d6935ef"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Completed...: 0 url [00:00, ? url/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f3c1148efd194daeae69b8dbcc24c9d5",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "f3c1148efd194daeae69b8dbcc24c9d5"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Extraction completed...: 0 file [00:00, ? file/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "dc7d6718b55b499ebde1bd8c3b77c108",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "dc7d6718b55b499ebde1bd8c3b77c108"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Extraction completed...: 0 file [00:00, ? file/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "024cbbb854c943319b9be0d743e09f84",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "024cbbb854c943319b9be0d743e09f84"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Generating train examples...: 0 examples [00:00, ? examples/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6c26c23d5e9544e8b114c816ad667320",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "6c26c23d5e9544e8b114c816ad667320"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train examples...: 0 examples [00:00, ? examples/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ec2a121d9157485d9002795e0c86d471",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ec2a121d9157485d9002795e0c86d471"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Dataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1. Subsequent calls will reuse this data.\n"
           ]
@@ -650,25 +642,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "dot_network = DLRM(\n",
-        "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
-        "    embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
-        ")\n",
-        "rmse, dot_network_num_params = train_and_evaluate(\n",
-        "    learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
-        "    epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
-        "    train_data=train_ds,\n",
-        "    test_data=test_ds,\n",
-        "    model= dot_network,\n",
-        "    plot_metrics=True\n",
-        ")\n",
-        "print_stats(\n",
-        "    rmse_list=[rmse],\n",
-        "    num_params=dot_network_num_params,\n",
-        "    model_name=\"Dot Network\",\n",
-        ")\n"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -677,11 +651,10 @@
         "id": "OAoTyyzi_Nau",
         "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Epoch 1/25\n",
             "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - loss: 8.1493 - root_mean_squared_error: 2.8203\n",
@@ -738,15 +711,34 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 1200x600 with 1 Axes>"
-            ],
-            "image/png": 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XXlFMTEzQ6wUAAAAANG2FhYX64x//qOzsbDVr1uyI21sauiMjI3XiiSdq6dKl/mU333yzVq1apWXLllXbvmpLd25urjIzM7Vv3z4lJCQ0SM1Hw+Vyac6cORoxYoQcDofV5QB1xjWMcMb1i3DG9YtwxvWLcHa46zc3N1cpKSnKycmpVQ61dCC1tLS0at3Du3fvrg8++KDG7Z1Op5xOZ7XlDocjLP4hh0udwKFwDSOccf0inHH9Ipxx/SKc1XT91vV6tnTKsEGDBmn9+vUBy3799Ve1bdvWoooAAAAAAKg/lobu2267TcuXL9fDDz+sjRs3aubMmXr55Zc1efJkK8sCAAAAAKBeWNq9vH///po9e7amTp2qP//5z2rfvr2eeuopjR8/3sqyAAAAgJBhmqbKysrkdrutLqVOXC6XIiIiVFxcHHa1o2mz2+2qz6HPLA3dknTWWWfprLPOsroMAAAAIOSUlpZq586dKiwstLqUOjNNU61atVJWVpYMw7C6HKBOoqKiajUHd21YHroBAAAAVOfxeLR582bZ7Xalp6crMjIyrMKrx+NRfn6+4uLi6i28AMFmmqZKS0u1Z88etWjRQh6P55iPSegGAAAAQlBpaak8Ho8yMzMVExNjdTl15vF4VFpaWq8thkBDiI6Olt1uV25urlwuV40zaNUFVz8AAAAQwgisQMPz/burj3u7+RcMAAAAAECQELoBAAAAAAgSQjcAAAAAAEFC6AYAAABQryZOnKjzzz/f6jKAkEDoBgAAAAAgSAjdAAAAQBgwTVOFpWWWPOpjBGefBQsW6KSTTpLT6VRaWpruuecelZWV+de///776t27t6Kjo9W8eXOdccYZKigokCTNnz9fJ510kmJjY5WYmKhBgwbp999/r7fagGBgnm4AAAAgDBS53OrxwJeWnPuXP49STOSxR4ft27dr7Nixmjhxot58802tW7dO1157raKiojRt2jTt3LlTl112mR5//HGdf/75ysvL06JFi2SapsrKynTeeefp2muv1dtvv63S0lKtXLlShmHUwycEgofQDQAAAKBBPP/888rMzNSzzz4rwzDUrVs37dixQ3fffbceeOAB7dy5U2VlZbrgggvUtm1bSVLv3r0lSQcOHFBOTo7OOussdezYUZLUvXt3yz4LUFuE7gawYXe+vt1nqNveAnVNT7S6HAAAAIShaIddv/x5lGXnrg9r167VwIEDA1qnBw0apPz8fG3btk19+/bV8OHD1bt3b40aNUojR47UH/7wByUlJSk5OVkTJ07UqFGjNGLECJ1xxhm6+OKLlZaWVi+1AcHCPd0N4Ln5v+mNDXbN/3Wv1aUAAAAgTBmGoZjICEseDdWF2263a86cOfr888/Vo0cPPfPMM+ratas2b94sSXr99de1bNkynXLKKZo1a5a6dOmi5cuXN0htwNEidDeAjKRoSVLWwSKLKwEAAACs0717dy1btixgYLYlS5YoPj5eGRkZkrxfLgwaNEjTp0/Xd999p8jISM2ePdu//fHHH6+pU6dq6dKl6tWrl2bOnNngnwOoC7qXNwBCNwAAAJqa3NxcrVmzRrGxsbLZvG191113nZ566ilNmTJFN910k9avX68HH3xQt99+u2w2m1asWKG5c+dq5MiRSk1N1YoVK7R37151795dmzdv1ssvv6xzzjlH6enpWr9+vTZs2KArr7zS4k8KHB6huwH4Qvc2QjcAAACaiPnz5+vUU08NWHbNNdfos88+05133qm+ffsqOTlZ11xzje677z5JUkJCghYuXKinnnpKubm5atu2rZ544gmNGTNGu3fv1rp16/TGG29o//79SktL0+TJk3X99ddb8fGAWiN0N4DM5IrQbZom0xoAAACgUZsxY4Zee+015ebmKiEhwd/S7bNy5coa9+vevbu++OKLGte1bNkyoJs5EC64p7sBpDeLkiFTJWUe7c0rsbocAAAAAEADIXQ3AIfdpsRI7+usg4XWFgMAAAAAaDCE7gbSPMo7QmPWAe7rBgAAAICmgtDdQJo7vc9bD9DSDQAAAABNBaG7gVS0dBO6AQAAAKCpIHQ3kOTylm7u6QYAAACApoPQ3UBSuKcbAAAAAJocQncD8bV078wpksvtsbYYAAAAAECDIHQ3kASH5IywyWNKO7Jp7QYAAACApoDQ3UAMQ8pIipZEF3MAAAAATcPEiRN13nnnWV2GpQjdDcgXupk2DAAAAI3VxIkTZRiG7Ha7WrRooY4dO+quu+5ScXFxvZ5n2LBhuvXWW2u1nWEYevTRR6utO/PMM2UYhqZNm1avtR2NwsJCTZ06VR07dlRUVJRatGihoUOH6qOPPrK6tKCaP3++DMOo8bFr1y6ry6sXEVYX0JRk+lq6GcEcAAAAjdjo0aP16quv6sCBA/r111911VVXyTAMPfbYY5bUk5mZqRkzZuiee+7xL9u+fbvmzp2rtLQ0S2qq6oYbbtCKFSv0zDPPqEePHtq/f7+WLl2q/fv3W12aSktLFRkZGdRzrF+/XgkJCQHLUlNT61SPy+WSw+Go87mPdr/aoqW7AVV0Lyd0AwAAoI5MUyotsOZhmnUq1el0qlWrVsrIyNB5552nM844Q3PmzPGvLykp0c0336zU1FRFRUVp8ODBWrVqVcAxFixYoJNOOklOp1NpaWm65557VFZWJsnbmr5gwQI9/fTT/lbRLVu2HLKes846S/v27dOSJUv8y9544w2NHDmyWrArKSnRHXfcodatWys2NlYDBgzQ/Pnz/ev379+vyy67TK1bt1ZMTIx69+6tt99+O+AYw4YN080336y77rpLycnJatWq1RFb0z/++GPde++9Gjt2rNq1a6d+/fppypQpuvrqq/3b7NmzR2effbaio6PVvn17vfXWW2rXrp2eeuopSdKWLVtkGIa+//57/z7Z2dkyDMP/Gdxut6655hq1b99e0dHR6tq1q55++umAWnxdwv/6178qPT1dXbt2lSRlZWXp4osvVmJiopKTk3XuuecG/Nzdbrduv/12JSYmqnnz5rrrrrtk1vLaSU1NVatWrQIeNpvtkPX4PuusWbM0dOhQRUVF6a233pLH49Gf//xnZWRkyOl06rjjjtMXX3zhP8+h9gsmWrobUEVLN/d0AwAAoI5chdLD6dac+94dUmTsUe36008/aenSpWrbtq1/2V133aUPPvhAb7zxhtq2bavHH39co0aN0saNG5WcnKzt27dr7Nixmjhxot58802tW7dO1157raKiojRt2jQ9/fTT+vXXX9WrVy/9+c9/liS1aNHikDVERkZq/Pjxev311zVo0CBJ0owZM/T4449XC8M33XSTfvnlF73zzjtKT0/X7NmzNXr0aK1Zs0adO3dWcXGx+vXrp7vvvlsJCQn673//qyuuuEIdO3bUSSed5D/OG2+8odtvv10rVqzQsmXLNHHiRA0aNEgjRoyoscZWrVrps88+0wUXXKD4+Pgat5k4caJ27NihefPmyeFw6Oabb9aePXtq9Xvw8Xg8ysjI0HvvvafmzZtr6dKluu6665SWlqaLL77Yv93cuXOVkJDg/7LE5XJp1KhRGjhwoBYtWqSIiAg99NBDGj16tH788UdFRkbqiSee0IwZM/Taa6+pe/fueuKJJzR79mydfvrpdaqxJlXr8bnnnnv0xBNP6Pjjj1dUVJSefvppPfHEE3rppZd0/PHH67XXXtM555yjn3/+WZ07dz7kfsFE6G5AtHQDAACgKfj000+VkJCgsrIylZSUyGaz6dlnn5UkFRQU6IUXXtCMGTM0ZswYSdK//vUvzZkzR6+++qruvPNOPf/888rMzNSzzz4rwzDUrVs37dixQ3fffbceeOABNWvWTJGRkYqJiVGrVq1qVdPVV1+tIUOG6Omnn9bq1auVk5Ojs846KyB0b926Va+//rq2bt2q9HTvFxx33HGHvvjiC73++ut6+OGH1bp1a91xxx3+faZMmaIvv/xS7777bkDo7tOnjx588EFJUufOnfXss89q7ty5hwzdL7/8ssaPH6/mzZurb9++Gjx4sP7whz/4vyT49ddf9fnnn2vlypXq37+/JOnVV19V9+7da/X5fRwOh6ZPn+5/3759ey1btkzvvvtuQOiOjY3VK6+84u/G/e9//1sej0evvPKKDMOQJL3++utKTEzU/PnzNXLkSD311FOaOnWqLrjgAknSiy++qC+//LJWdWVkZAS8b9u2rX7++edD1uNrYb/11lv955Okv//977r77rt16aWXSpIee+wxzZs3T0899ZSee+45/3ZV9wsmQncD8rV0HygoVUFJmWKd/PgBAABQS44Yb4uzVeeug9NOO03PPfecdu/erVdeeUUOh0MXXnihJGnTpk1yuVz+MCl5g+BJJ52ktWvXSpLWrl2rgQMH+sOdJA0aNEj5+fnatm2b2rRpU+eP0LdvX3Xu3Fnvv/++5s2bpyuuuEIREYH/P75mzRq53W516dIlYHlJSYmaN28uyduF+uGHH9a7776r7du3q7S0VCUlJYqJCfwZ9enTJ+B9WlraYVulTz31VP32229avny5li5dqrlz5+rpp5/W9OnTdf/992vt2rWKiIhQv379/Pt069ZNiYmJdf5ZPPfcc3rttde0detWFRUVqbS0VMcdd1zANr179w64b/qHH37Qxo0bq7XCFxcXa9OmTcrJydHOnTs1YMAA/7qIiAideOKJtepivmjRooBjV73Humo9PieeeKL/dW5urnbs2BFwbUnea+eHH3445H7BRuprQPFRDiXGOJRd6FLWwUJ1a5Vw5J0AAAAAyTsH7VF28W5osbGx6tSpk1JTU/Xqq6/q+OOP16uvvqprrrnG0rquvvpqPffcc/rll1+0cuXKauvz8/Nlt9u1evVq2e32gHVxcXGSpL/97W96+umn9dRTT6l3796KjY3VrbfeqtLS0oDtq4ZGwzDk8XgOW5/D4dCQIUM0ZMgQ3X333XrooYf05z//WXfffXetPp/vHujKIdflcgVs88477+iOO+7QE088oYEDByo+Pl5/+9vftGLFioDtYmMDr7X8/Hz169evxvufD9e1v7bat29/2C8QqtZzpOVHcrT7HQ0GUmtgmUneb8C27qeLOQAAABo/m82me++9V/fdd5+KiorUsWNHRUZGBgxq5nK5tGrVKvXo0UOS1L17dy1btiwgPC5ZskTx8fH+bsiRkZFyu911qmXcuHFas2aNevXq5T9XZccff7zcbrf27NmjTp06BTx83diXLFmic889V5dffrn69u2rDh066Ndff63zz6U2evToobKyMhUXF6tbt24qKyvT6tWr/evXr1+v7Oxs/3tf+N25c6d/WeVB1Xz1n3LKKZo0aZKOP/54derUSZs2bTpiLSeccII2bNig1NTUaj+bZs2aqVmzZkpLSwsI71XrDbaEhASlp6cHXFuS9zPX9PtuKITuBpaZzGBqAAAAaFouuugi2e12Pffcc4qNjdWNN96oO++8U1988YV++eUXXXvttSosLPS3hE+aNElZWVmaMmWK1q1bp48++kgPPvigbr/9dn9rbrt27bRixQpt2bJF+/btO2IrsiQlJSVp586dmjt3bo3ru3TpovHjx+vKK6/Uhx9+qM2bN2vlypV65JFH9N///leS9/7sOXPmaOnSpVq7dq2uv/567d69+5h/RsOGDdNLL72k1atXa8uWLfrss89077336rTTTlNCQoK6du2q0aNH6/rrr9eKFSu0evVq/fGPf1R0dLT/GNHR0Tr55JP16KOPau3atVqwYIHuu+++gPN07txZ33zzjb788kv9+uuvuv/++6uNHF+T8ePHKyUlReeee64WLVqkzZs3a/78+br55pu1bds2SdItt9yiRx99VP/5z3+0bt06TZo0KeBLgcPZs2ePdu3aFfCo2kpfG3feeacee+wxzZo1S+vXr9c999yj77//Xrfcckudj1VfCN0NzNfSzWBqAAAAaCoiIiJ000036fHHH1dBQYEeffRRXXjhhbriiit0wgknaOPGjfryyy+VlJQkSWrdurU+++wzrVy5Un379tUNN9yga665JiBA3nHHHbLb7erRo4datGihrVu31qqWxMTEw3Ytfv3113XllVfqT3/6k7p27arzzjtPq1at8t9Hft999+mEE07QqFGjNGzYMLVq1UrnnXfe0f9wyo0aNco/jVn37t01ZcoUjRo1Su+++25Abenp6Ro6dKguuOACXXfdddWmPHvttddUVlamfv366dZbb9VDDz0UsP7666/XBRdcoEsuuUQDBgzQ/v37NWnSpCPWFxMTo4ULF6pNmza64IIL1L17d11zzTUqLi72z6/9pz/9SVdccYUmTJjg77p+/vnn1+rzd+3aVWlpaQGPo2klv/nmm3X77bfrT3/6k3r37q0vvvhCH3/8ccDI5Q3NMGs7cVoIys3NVbNmzZSTk1NtIvVQ4nK59Nlnn2ns2LGatXqH7vvPTzqje6pemdDf6tKAWql8DVe9PwkIdVy/CGdcv01bcXGxNm/erPbt2wd9SqNg8Hg8ys3NVUJCgr91GvWvXbt2uvXWW3XrrbdaXUqjUlhYqLVr16pLly7VBo+raw7l6m9gmcnl93TT0g0AAAAAjR6hu4Fl+ufqLqrV0PkAAAAAgPDFlGENrHVStAxDKnK5tb+gVClxTqtLAgAAABCmtmzZYnUJOAJauhuYM8KuVgnee3IYTA0AAAAAGjdCtwX8c3UTugEAAHAE3JIINDzfvzvDMI75WIRuC2SUz9W9jbm6AQAAcAi+EesLC2moARpaYWGhPB6PIiKO/Y5s7um2QJtk5uoGAADA4dntdiUmJmrPnj2SvPMk10erW0PxeDwqLS1VcXExU4YhbJimqcLCQu3du1d5eXmy2+3HfExCtwXoXg4AAIDaaNWqlST5g3c4MU1TRUVFio6ODqsvCwBJSkhI0IYNG+rlWIRuC/jm6s46SOgGAADAoRmGobS0NKWmpsrlclldTp24XC4tXLhQp556qr+rPBAOHA6HPB5PvR2P0G0BX/fyHdnFKnN7FGGnuw0AAAAOzW6310s314Zkt9tVVlamqKgoQjfCTn2GbtKeBVLjnYqMsMntMbUzp9jqcgAAAAAAQULotoDNZigj0TuCOYOpAQAAAEDjRei2SAb3dQMAAABAo0fotkibZF9LN3N1AwAAAEBjRei2CNOGAQAAAEDjR+i2CNOGAQAAAEDjR+i2iG/aMLqXAwAAAEDjRei2iK97+b78EhWVui2uBgAAAAAQDIRuizSLcSg+KkISXcwBAAAAoLEidFvI19rNXN0AAAAA0DgRui1UcV83oRsAAAAAGiNCt4Uyy+fq3spgagAAAADQKBG6LcS0YQAAAADQuBG6LcQ93QAAAADQuBG6LeRr6d52sEimaVpcDQAAAACgvhG6LZSR5L2nO7+kTAcLXRZXAwAAAACob4RuC0U57EqNd0qiizkAAAAANEaEbou1YTA1AAAAAGi0CN0W893XvZWWbgAAAABodAjdFsssv687i7m6AQAAAKDRIXRbLMM/gjkt3QAAAADQ2FgauqdNmybDMAIe3bp1s7KkBue/p5vu5QAAAADQ6ERYXUDPnj311Vdf+d9HRFheUoPy3dO9PbtIbo8pu82wuCIAAAAAQH2xPOFGRESoVatWVpdhmVYJUXLYDbncpnblFqt1YrTVJQEAAAAA6onloXvDhg1KT09XVFSUBg4cqEceeURt2rSpcduSkhKVlJT43+fm5kqSXC6XXC5Xg9R7NHy1HarG9GbR+v1AoTbvyVVqrOW/EqCaI13DQCjj+kU44/pFOOP6RTg73PVb12vaME3TrJeqjsLnn3+u/Px8de3aVTt37tT06dO1fft2/fTTT4qPj6+2/bRp0zR9+vRqy2fOnKmYmJiGKDkonv/FpvU5No3r6NaAVMt+HQAAAACAIygsLNS4ceOUk5OjhISEI25vaeiuKjs7W23bttWTTz6pa665ptr6mlq6MzMztW/fvlp9WKu4XC7NmTNHI0aMkMPhqLb+vo9+0axvtmnysA66dXgnCyoEDu9I1zAQyrh+Ec64fhHOuH4Rzg53/ebm5iolJaXWoTuk+jInJiaqS5cu2rhxY43rnU6nnE5nteUOhyMs/iEfqs62KbGSpB05JWHxOdB0hcu/NaAmXL8IZ1y/CGdcvwhnNV2/db2eQ2qe7vz8fG3atElpaWlWl9KgmDYMAAAAABonS0P3HXfcoQULFmjLli1aunSpzj//fNntdl122WVWltXgMpO8oXsroRsAAAAAGhVLu5dv27ZNl112mfbv368WLVpo8ODBWr58uVq0aGFlWQ3ON1f3nrwSFbvcinLYLa4IAAAAAFAfLA3d77zzjpWnDxlJMQ7FOSOUX1KmbQeL1Ck1zuqSAAAAAAD1IKTu6W6qDMNQRlK0JCnrIF3MAQAAAKCxIHSHiEwGUwMAAACARofQHSJ8g6kRugEAAACg8SB0h4g2yeXdyw8UWVwJAAAAAKC+ELpDhK97OdOGAQAAAEDjQegOEf57uhlIDQAAAAAaDUJ3iPDd051XXKacQpfF1QAAAAAA6gOhO0RER9qVEueURGs3AAAAADQWhO4Qklk+mBr3dQMAAABA40DoDiFMGwYAAAAAjQuhO4S0YTA1AAAAAGhUCN0hpKJ7OXN1AwAAAEBjQOgOIb7u5dvoXg4AAAAAjQKhO4T45uredrBIHo9pcTUAAAAAgGNF6A4hac2iZLcZKnV7tCevxOpyAAAAAADHiNAdQiLsNqUnRkli2jAAAAAAaAwI3SGGacMAAAAAoPEgdIcYpg0DAAAAgMaD0B1ifIOp0b0cAAAAAMIfoTvEZCR55+rexlzdAAAAABD2CN0hhu7lAAAAANB4ELpDjK97+a7cYpWUuS2uBgAAAABwLAjdIaZ5bKSiHXaZprT9IF3MAQAAACCcEbpDjGEYykz23tedRegGAAAAgLBG6A5B/vu6GcEcAAAAAMIaoTsEZSQRugEAAACgMSB0h6BMRjAHAAAAgEaB0B2CMsvn6s5irm4AAAAACGuE7hDUpjkt3QAAAADQGBC6Q1Bm+T3d2YUu5Ra7LK4GAAAAAHC0CN0hKNYZoeTYSEkMpgYAAAAA4YzQHaL8g6lxXzcAAAAAhC1Cd4jyDaa2jfu6AQAAACBsEbpDlK+leyvdywEAAAAgbBG6Q5RvMDXu6QYAAACA8EXoDlFtfPd0H+SebgAAAAAIV4TuEJWZ7L2nO+tAoUzTtLgaAAAAAMDRIHSHqPTEaNkMqaTMo715JVaXAwAAAAA4CoTuEOWw25TWrLy1mxHMAQAAACAsEbpDWEUXc+7rBgAAAIBwROgOYb4RzJk2DAAAAADCE6E7hPnm6mbaMAAAAAAIT4TuEFYxbRihGwAAAADCEaE7hHFPNwAAAACEN0J3CPPd070zp0gut8fiagAAAAAAdUXoDmEt4p1yRtjkMaUd2bR2AwAAAEC4IXSHMMMwKg2mRugGAAAAgHBD6A5xmUne+7qZNgwAAAAAwg+hO8RlMoI5AAAAAIQtQneIa8Nc3QAAAAAQtgjdIS4jidANAAAAAOGK0B3i/HN1H2QgNQAAAAAIN4TuEOe7p/tAQakKSsosrgYAAAAAUBeE7hCXEOVQYoxDEoOpAQAAAEC4IXSHgczy+7q37id0AwAAAEA4IXSHAe7rBgAAAIDwROgOA5lMGwYAAAAAYYnQHQYymTYMAAAAAMISoTsM+Fu6GUgNAAAAAMIKoTsMtPF3Ly+SaZoWVwMAAAAAqC1CdxhIT4ySYUhFLrf2F5RaXQ4AAAAAoJYI3WHAGWFXq4QoSdJW7usGAAAAgLBB6A4TDKYGAAAAAOGH0B0mfIOpbWOubgAAAAAIG4TuMJGZHC2Jlm4AAAAACCeE7jDh617OPd0AAAAAED4I3WGiTXPm6gYAAACAcEPoDhO+lu4d2cUqc3ssrgYAAAAAUBuE7jCRGu9UZIRNbo+pnTnFVpcDAAAAAKiFkAndjz76qAzD0K233mp1KSHJZjOUkchgagAAAAAQTkIidK9atUovvfSS+vTpY3UpIc03bRj3dQMAAABAeLA8dOfn52v8+PH617/+paSkJKvLCWkV04YxVzcAAAAAhIMIqwuYPHmyzjzzTJ1xxhl66KGHDrttSUmJSkpK/O9zc3MlSS6XSy6XK6h1HgtfbcdaY3ozpyRpy778kP68aHzq6xoGrMD1i3DG9YtwxvWLcHa467eu17Slofudd97Rt99+q1WrVtVq+0ceeUTTp0+vtvx///ufYmJi6ru8ejdnzpxj2n/vfkOSXT9t3qnPPttWP0UBdXCs1zBgJa5fhDOuX4Qzrl+Es5qu38LCut3ua1nozsrK0i233KI5c+YoKiqqVvtMnTpVt99+u/99bm6uMjMzNXLkSCUkJASr1GPmcrk0Z84cjRgxQg6H46iP03ZHrl7/dbny5dTYscPqr0DgCOrrGgaswPWLcMb1i3DG9Ytwdrjr19fjurYsC92rV6/Wnj17dMIJJ/iXud1uLVy4UM8++6xKSkpkt9sD9nE6nXI6ndWO5XA4wuIf8rHW2b6F94uFffmlcpmGYiItvzsATUy4/FsDasL1i3DG9YtwxvWLcFbT9VvX69my1DZ8+HCtWbMmYNlVV12lbt266e67764WuCE1i3EoPipCecVl2nawSF1axltdEgAAAADgMCwL3fHx8erVq1fAstjYWDVv3rzaclRokxyjn3fkKutAIaEbAAAAAEKc5VOGoW4yk8rn6j7AXN0AAAAAEOpC6qbg+fPnW11CyPPN1b2VuboBAAAAIOTR0h1mMpPLW7oP0tINAAAAAKGO0B1m/KGb7uUAAAAAEPII3WGm8j3dpmlaXA0AAAAA4HAI3WEmI8l7T3dBqVsHC10WVwMAAAAAOBxCd5iJctjVMsEpiS7mAAAAABDqCN1hyN/FnMHUAAAAACCkEbrDkG8wta20dAMAAABASCN0h6HM8vu6s5irGwAAAABCGqE7DPlaurfRvRwAAAAAQhqhOwzRvRwAAAAAwgOhOwz5QveO7CK5PczVDQAAAAChitAdhlolRMlhN+Rym9qVW2x1OQAAAACAQyB0hyG7zVDrRN9ganQxBwAAAIBQRegOU9zXDQAAAAChj9AdpjKSykcwJ3QDAAAAQMgidIepNuUt3VkHmasbAAAAAEIVoTtMZSZ77+mmezkAAAAAhC5Cd5jKLO9ezkBqAAAAABC6CN1hyte9fE9eiYpdbourAQAAAADUhNAdphJjHIpzRkiStnFfNwAAAACEJEJ3mDIMQxlJzNUNAAAAAKHsmEJ3cXFxfdWBo5DpH8Gc0A0AAAAAoajOodvj8egvf/mLWrdurbi4OP3222+SpPvvv1+vvvpqvReIQ/NPG0ZLNwAAAACEpDqH7oceekgzZszQ448/rsjISP/yXr166ZVXXqnX4nB4mf7u5dzTDQAAAAChqM6h+80339TLL7+s8ePHy263+5f37dtX69atq9ficHi+7uXM1Q0AAAAAoanOoXv79u3q1KlTteUej0cul6teikLttOGebgAAAAAIaXUO3T169NCiRYuqLX///fd1/PHH10tRqJ2MJG/ozisuU04hX3gAAAAAQKiJqOsODzzwgCZMmKDt27fL4/Howw8/1Pr16/Xmm2/q008/DUaNOIToSLtS4pzal1+irQcK1TummdUlAQAAAAAqqXNL97nnnqtPPvlEX331lWJjY/XAAw9o7dq1+uSTTzRixIhg1IjDyEwuH0yNLuYAAAAAEHLq3NItSUOGDNGcOXPquxYchTbJMfpuazbThgEAAABACKpzSzdCS2YSg6kBAAAAQKiqc0u3zWaTYRiHXO92u4+pINSNr3v5VubqBgAAAICQU+fQPXv27ID3LpdL3333nd544w1Nnz693gpD7fjm6t5G93IAAAAACDl1Dt3nnntutWV/+MMf1LNnT82aNUvXXHNNvRSG2vF1L992sEgejymb7dC9EAAAAAAADave7uk++eSTNXfu3Po6HGoprVmU7DZDpW6PducVW10OAAAAAKCSegndRUVF+uc//6nWrVvXx+FQBxF2m9IToyRJWdzXDQAAAAAhpc7dy5OSkgIGUjNNU3l5eYqJidG///3vei0OtdMmOUZZB4qUdaBQJ7VPtrocAAAAAEC5Oofuf/zjHwGh22azqUWLFhowYICSkpLqtTjUjve+7v1MGwYAAAAAIabOoXvixIlBKAPHwjeC+VZGMAcAAACAkFKr0P3jjz/W+oB9+vQ56mJwdDKSvHN1b+OebgAAAAAIKbUK3ccdd5wMw5BpmofdzjAMud3ueikMtdemvKWb7uUAAAAAEFpqFbo3b94c7DpwDHzdy3flFqukzC1nhN3iigAAAAAAUi1Dd9u2bYNdB45B89hIRTvsKnK5tf1gkTq0iLO6JAAAAACAjmIgNZ9ffvlFW7duVWlpacDyc84555iLQt0YhqE2yTFavztPWYRuAAAAAAgZdQ7dv/32m84//3ytWbMm4D5v3zRi3NNtjczkaG/oZgRzAAAAAAgZtrrucMstt6h9+/bas2ePYmJi9PPPP2vhwoU68cQTNX/+/CCUiNrISCofTI3QDQAAAAAho84t3cuWLdPXX3+tlJQU2Ww22Ww2DR48WI888ohuvvlmfffdd8GoE0eQyQjmAAAAABBy6tzS7Xa7FR8fL0lKSUnRjh07JHkHW1u/fn39Voda808bxlzdAAAAABAy6tzS3atXL/3www9q3769BgwYoMcff1yRkZF6+eWX1aFDh2DUiFrITI6WJG2lezkAAAAAhIw6h+777rtPBQUFkqQ///nPOuusszRkyBA1b95cs2bNqvcCUTuZ5fd05xS5lFvsUkKUw+KKAAAAAAC1Dt0nnnii/vjHP2rcuHFKSEiQJHXq1Enr1q3TgQMHlJSU5B/BHA0v1hmh5rGR2l9QqqwDheqZ3szqkgAAAACgyav1Pd19+/bVXXfdpbS0NF155ZUBI5UnJycTuENABvd1AwAAAEBIqXXofvXVV7Vr1y4999xz2rp1q4YPH65OnTrp4Ycf1vbt24NZI2opM8l7XzfThgEAAABAaKjT6OUxMTGaOHGi5s+fr19//VWXXnqpXnrpJbVr105nnnmmPvzww2DViVpg2jAAAAAACC11njLMp2PHjnrooYe0ZcsWvf3221q+fLkuuuii+qwNdVQxbRihGwAAAABCQZ1HL69s/vz5ev311/XBBx8oIiJC1157bX3VhaPgG8E86yD3dAMAAABAKKhz6N62bZtmzJihGTNm6LffftOQIUP0/PPP66KLLlJ0dHQwakQt+ebqzjpQKNM0GdwOAAAAACxW69D97rvv6rXXXtPcuXOVmpqqCRMm6Oqrr1anTp2CWR/qID0xWjZDKinzaG9eiVIToqwuCQAAAACatFqH7ssvv1xnnnmmZs+erbFjx8pmO+rbwREkDrtNac2itT27SFkHCwndAAAAAGCxWofubdu2KTU1NZi1oB5kJntD99YDherXNtnqcgAAAACgSat1czWBOzz4B1M7wGBqAAAAAGA1+og3MkwbBgAAAAChg9DdyGT6QvdBQjcAAAAAWI3Q3chUTBtG93IAAAAAsFqdQ/eqVau0YsWKastXrFihb775pl6KwtHztXTvzCmSy+2xuBoAAAAAaNrqHLonT56srKysasu3b9+uyZMn10tROHot4pyKctjkMaUd2bR2AwAAAICV6hy6f/nlF51wwgnVlh9//PH65Zdf6qUoHD3DMJRRPoL5VgZTAwAAAABL1Tl0O51O7d69u9rynTt3KiKi1tN+I4gyk7ivGwAAAABCQZ1D98iRIzV16lTl5OT4l2VnZ+vee+/ViBEj6rU4HJ02jGAOAAAAACGhzqH773//u7KystS2bVuddtppOu2009S+fXvt2rVLTzzxRJ2O9cILL6hPnz5KSEhQQkKCBg4cqM8//7yuJaGKTObqBgAAAICQUOf+4K1bt9aPP/6ot956Sz/88IOio6N11VVX6bLLLpPD4ajTsTIyMvToo4+qc+fOMk1Tb7zxhs4991x999136tmzZ11LQznfPd2EbgAAAACw1lHdhB0bG6vrrrvumE9+9tlnB7z/61//qhdeeEHLly8ndB+Diu7l3NMNAAAAAFaqVej++OOPNWbMGDkcDn388ceH3facc845qkLcbrfee+89FRQUaODAgTVuU1JSopKSEv/73NxcSZLL5ZLL5Tqq8zYEX20NVWOreG+PgwMFpTqYX6Q4JwPc4dg09DUM1CeuX4Qzrl+EM65fhLPDXb91vaYN0zTNI21ks9m0a9cupaamymY79G3ghmHI7XbXqYA1a9Zo4MCBKi4uVlxcnGbOnKmxY8fWuO20adM0ffr0astnzpypmJiYOp23sZu6yq7CMkN39SlT61irqwEAAACAxqGwsFDjxo1TTk6OEhISjrh9rUJ3MJWWlmrr1q3KycnR+++/r1deeUULFixQjx49qm1bU0t3Zmam9u3bV6sPaxWXy6U5c+ZoxIgRdb7v/Wid/8Jy/bQjVy+MO05ndE9tkHOi8bLiGgbqC9cvwhnXL8IZ1y/C2eGu39zcXKWkpNQ6dNep37HL5dLo0aP14osvqnPnznWr+hAiIyPVqVMnSVK/fv20atUqPf3003rppZeqbet0OuV0OqstdzgcYfEPuSHrbNs8Vj/tyNWO3NKw+NkgPITLvzWgJly/CGdcvwhnXL8IZzVdv3W9nus0ZZjD4dCPP/5YpxPUlcfjCWjNxtHJSI6WxAjmAAAAAGClOs/Tffnll+vVV1+tl5NPnTpVCxcu1JYtW7RmzRpNnTpV8+fP1/jx4+vl+E1ZJtOGAQAAAIDl6jysdVlZmV577TV99dVX6tevn2JjA0fpevLJJ2t9rD179ujKK6/Uzp071axZM/Xp00dffvmlRowYUdeyUEXFtGGEbgAAAACwSp1D908//aQTTjhBkvTrr78e08nrq8Uc1WX6QveBIpmmKcMwLK4IAAAAAJqeOofuefPmBaMO1LP0xCgZhlTkcmtffqlaxFcfgA4AAAAAEFx1vqf76quvVl5eXrXlBQUFuvrqq+ulKBw7Z4RdrRKiJNHFHAAAAACsUufQ/cYbb6ioqKja8qKiIr355pv1UhTqR0UXc0I3AAAAAFih1t3Lc3NzZZqmTNNUXl6eoqKi/Ovcbrc+++wzpaamBqVIHJ3MpBit3HxA2w5W/5IEAAAAABB8tQ7diYmJMgxDhmGoS5cu1dYbhqHp06fXa3E4Npnlc3Vv3U9LNwAAAABYodahe968eTJNU6effro++OADJScn+9dFRkaqbdu2Sk9PD0qRODr+ubq5pxsAAAAALFHr0D106FBJ0ubNm9WmTRumoAoDbZoTugEAAADASnUeSK1t27ZavHixLr/8cp1yyinavn27JOn//b//p8WLF9d7gTh6vpbuHdnFKnN7LK4GAAAAAJqeOofuDz74QKNGjVJ0dLS+/fZblZSUSJJycnL08MMP13uBOHqp8U5FRtjk9pjamVNsdTkAAAAA0OTUOXQ/9NBDevHFF/Wvf/1LDofDv3zQoEH69ttv67U4HBubzVBGkncwNaYNAwAAAICGV+fQvX79ep166qnVljdr1kzZ2dn1URPqEYOpAQAAAIB16hy6W7VqpY0bN1ZbvnjxYnXo0KFeikL98U8bRks3AAAAADS4Oofua6+9VrfccotWrFghwzC0Y8cOvfXWW7rjjjt04403BqNGHAN/S/eBIosrAQAAAICmp9ZThvncc8898ng8Gj58uAoLC3XqqafK6XTqjjvu0JQpU4JRI45Bm2S6lwMAAACAVeocug3D0P/93//pzjvv1MaNG5Wfn68ePXooLi4uGPXhGGUm09INAAAAAFapc+j2iYyMVI8ePeqzFgSBr3v5vvwSFZaWKSbyqH/lAAAAAIA6qnUCu/rqq2u13WuvvXbUxaD+NYtxKCEqQrnFZdp2sEhdWsZbXRIAAAAANBm1Dt0zZsxQ27Ztdfzxx8s0zWDWhHqWmRyjn3fkKutAIaEbAAAAABpQrUP3jTfeqLffflubN2/WVVddpcsvv1zJycnBrA31JDPJG7qZNgwAAAAAGlatpwx77rnntHPnTt1111365JNPlJmZqYsvvlhffvklLd8hzjdXN4OpAQAAAEDDqtM83U6nU5dddpnmzJmjX375RT179tSkSZPUrl075efnB6tGHCOmDQMAAAAAa9QpdAfsaLPJMAyZpim3212fNaGeZfinDSN0AwAAAEBDqlPoLikp0dtvv60RI0aoS5cuWrNmjZ599llt3bqVebpDmG/asKwDhdwKAAAAAAANqNYDqU2aNEnvvPOOMjMzdfXVV+vtt99WSkpKMGtDPclI8t7TXVDq1sFCl5JjIy2uCAAAAACahlqH7hdffFFt2rRRhw4dtGDBAi1YsKDG7T788MN6Kw71I8phV8sEp3bnlijrQCGhGwAAAAAaSK1D95VXXinDMIJZC4IoMylGu3NLtPVAofpmJlpdDgAAAAA0CbUO3TNmzAhiGQi2zOQYffP7QUYwBwAAAIAGdNSjlyO8ZPpHMGeubgAAAABoKITuJiKzfDC1bbR0AwAAAECDIXQ3Eb6W7q3M1Q0AAAAADYbQ3US0KQ/dO7KL5PYwVzcAAAAANARCdxPRMiFKDrshl9vUrtxiq8sBAAAAgCaB0N1E2G2GWid67+veup8u5gAAAADQEAjdTYh/BHMGUwMAAACABkHobkJ8oXsbg6kBAAAAQIMgdDchmUm+lm7m6gYAAACAhkDobkIyk8vv6aalGwAAAAAaBKG7CfFNG5ZF6AYAAACABkHobkJ83cv35JWo2OW2uBoAAAAAaPwI3Q3E5im1ugQlxjgU54yQJG1jBHMAAAAACDpCdwOwrXxRI3++Tcb2byytwzAMZSR57+vOOsBgagAAAAAQbITuBmDs/lnOsjzZFjxqdSkV93XT0g0AAAAAQUfobgDuIXfII7tsm+dLvy+1tJZMBlMDAAAAgAZD6G4IiW21tfmp3tdf/1UyTctK8bV0/7gtx7IaAAAAAKCpIHQ3kPWtzpFpj5R+XyxtXmBZHad3S5XdZmjF5gP6butBy+oAAAAAgKaA0N1AiiOby3P8BO8bC1u7M5NjdP7xrSVJT8/dYEkNAAAAANBUELobkOeUW6SIKGnbSmnjV5bVcdNpnWS3GZq/fq9+yMq2rA4AAAAAaOwI3Q0pvpXU/4/e118/ZFlrd7uUWJ17XLok6Z+0dgMAAABA0BC6G9rg2yRHrLTze2ndfy0r46bTOslmSHPX7dEaBlUDAAAAgKAgdDe02BRpwPXe1/MeljweS8ro0CJO5/T1tnZzbzcAAAAABAeh2wqnTJGcCdKen6Vf/mNZGTed3lmGIX21drd+2k5rNwAAAADUN0K3FWKSpZMneV/Pf1TyuC0po1NqnM7u423tfuZrWrsBAAAAoL4Ruq0ycJIUlSjtWy+ted+yMqac3kmGIX35826t3ZlrWR0AAAAA0BgRuq0S1czbzVySFjwqucssKaNzy3iN7Z0midZuAAAAAKhvhG4rDbhBimkuHfhN+uFty8q4+fTOkqTP1uzS+l15ltUBAAAAAI0NodtKzjhp0K3e1wsel8pKLSmja6t4je3dSpL0T1q7AQAAAKDeELqt1v+PUlxLKWer9N3/s6yMKf7W7p3asJvWbgAAAACoD4Ruq0XGSEP+5H298O+Sq9iSMrqnJWhUz5YyTemZrzdaUgMAAAAANDaE7lBwwgQpobWUt0NaPcOyMm4e7m3t/uTHHdq4J9+yOgAAAACgsSB0hwJHlHTqHd7Xi56QSgstKaNnejON6OFt7X6We7sBAAAA4JgRukPFcZdLiW2kgj3SqlcsK8M3kvnHP+zQb3tp7QYAAACAY0HoDhURkdLQu72vlzwllVgzmFnvjGYa3i1VHlN6dh73dgMAAADAsSB0h5I+l0rJHaXC/dKKFy0r45YzvK3dH32/Q1v2FVhWBwAAAACEO0J3KLFHSMPu8b5e+oxUlG1JGX0yEnVa1xZye0xauwEAAADgGBC6Q02vC6UW3aTiHGn585aV4RvJfPZ327V1vzUDuwEAAABAuCN0hxqbvaK1e9nzUuEBS8o4vk2STu3ibe1+jtZuAAAAADgqhO5Q1P1cqWUvqTRPWvpPy8q4pby1+4NvtynrAK3dAAAAAFBXhO5QZLNJp93rfb3iJSl/ryVl9GubpMGdUlTmMfX8fFq7AQAAAKCuCN2hqutYKf14yVXonULMIr6RzN9fvU3bDtLaDQAAAAB1YWnofuSRR9S/f3/Fx8crNTVV5513ntavX29lSaHDMKTT7vO+XvWKlLvTkjL6t0vWKR2by+U29cL8TZbUAAAAAADhytLQvWDBAk2ePFnLly/XnDlz5HK5NHLkSBUUMDe0JKnTcClzgFRWLC1+0rIyfPd2v/tNlnZkF1lWBwAAAACEG0tD9xdffKGJEyeqZ8+e6tu3r2bMmKGtW7dq9erVVpYVOgxDOu3/vK9Xz5CysywpY0CH5hrQPlkut6kXF9DaDQAAAAC1FWF1AZXl5ORIkpKTk2tcX1JSopKSEv/73NxcSZLL5ZLL5Qp+gUfJV9tR1Zh5iuxtB8n2+xK5Fzwuz1hrWrxvGtZBKzYf0Nsrt+rawW3VKiHKkjpgjWO6hgGLcf0inHH9Ipxx/SKcHe76res1bZimadZLVcfI4/HonHPOUXZ2thYvXlzjNtOmTdP06dOrLZ85c6ZiYmKCXaJlkvPXa8iGv8oju+b2eEyFztQGr8E0pWd+tmtTnqFTW3l0YXtPg9cAAAAAAFYrLCzUuHHjlJOTo4SEhCNuHzKh+8Ybb9Tnn3+uxYsXKyMjo8ZtamrpzszM1L59+2r1Ya3icrk0Z84cjRgxQg6H46iOYX/7Itl+mydPn8vkPvuZeq6wdpZu2q8JM1YrMsKmr28brJa0djcZ9XENA1bh+kU44/pFOOP6RTg73PWbm5urlJSUWofukOheftNNN+nTTz/VwoULDxm4JcnpdMrpdFZb7nA4wuIf8jHVefr90m/zZFszS7ZT75BSOtVvcbVwateW6tc2Sat/P6jXlmbpgbN7NHgNsFa4/FsDasL1i3DG9YtwxvWLcFbT9VvX69nSgdRM09RNN92k2bNn6+uvv1b79u2tLCe0ZfSTuoyWTI80/xFLSjAMwz+S+VsrfteevGJL6gAAAACAcGFp6J48ebL+/e9/a+bMmYqPj9euXbu0a9cuFRUxLVWNTrvX+/zTB9LuXywpYUjnFB3fJlElZR69vOA3S2oAAAAAgHBhaeh+4YUXlJOTo2HDhiktLc3/mDVrlpVlha60vlL3syWZlrZ231ze2v3vFb9rX37JEfYAAAAAgKbL8u7lNT0mTpxoZVmhbdi9kgxp7cfSzh+tKaFLC/XNaKZil0f/WkhrNwAAAAAciqWhG0ehZQ+p1wXe1/MetqQEwzB0yxne1u43l/2u/bR2AwAAAECNCN3haNhUybBJv34ubVttSQmndU1V79bNVORy61+LNltSAwAAAACEOkJ3OErpLPW51Pt63l8tKaHyvd1vLtuiAwWlltQBAAAAAKGM0B2uht4l2SKkTXOlrcstKeGM7qnqmZ6gwlK3Xl3Mvd0AAAAAUBWhO1wlt5eOG+99/fVDlpRQubX7jaW/K7uQ1m4AAAAAqIzQHc5OvVOyR0pbFkmbF1pSwsgeLdU9LUH5JWV6dTH3dgMAAABAZYTucJaYKZ0wwfv6679KptngJRiGoZtP7yRJmrFki3IKXQ1eAwAAAACEKkJ3uBvyJykiSspa7r2/2wKjerZS15bxyisp02tLaO0GAAAAAB9Cd7hLSJNOvMb72qLWbput4t7u15ZsVk4Rrd0AAAAAIBG6G4fBt0mOGGnHt9KvX1hSwpherdQ5NU55xWV6Y+kWS2oAAAAAgFBD6G4M4lpIJ13nff31XyWPp8FLsNkMTSlv7X518WblFdPaDQAAAACE7sZi0C1SZLy0e4209mNLSjizd5o6tohVTpGL1m4AAAAAEKG78YhJlk6+0ft6/iOSx93gJdgr3dv9yuLNyi8pa/AaAAAAACCUELobk4GTpahm0t510k8fWlLCWX3S1SElVtmFLr25bIslNQAAAABAqCB0NybRidIpU7yv5z8iuRu+pdluM3RT+bzd/1r4mwpo7QYAAADQhBG6G5sBN0jRydKBTdKPsywp4Zy+6WrXPEYHC136f8t/t6QGAAAAAAgFhO7GxhkvDb7V+3rBY5K74UcRj7DbdNPp3nu7/7XwNxWW0toNAAAAoGkidDdG/a+VYlOl7N+l7/5tSQnnHZeuNskx2l9QqreWb7WkBgAAAACwGqG7MYqMkYbc7n298O9SWUmDlxBht+mm07z3dr+0cJOKSht+NHUAAAAAsBqhu7Hqd5UUny7lbpNWv2FJCeef0FoZSdHal1+qt1ZwbzcAAACApofQ3Vg5oqRT/+R9vegJyVXU8CXYbZrsb+3+TcUuWrsBAAAANC2E7sbs+CulZm2k/F3SqlctKeHCEzLUOjFae/NK9PZK7u0GAAAA0LQQuhuziEhp6J3e14v/IZXkN3gJkRE2TTqtoyTpxQWbaO0GAAAA0KQQuhu7vpdJSe2lwn3SypctKeEP/TKU3ixKu3NLNGtVliU1AAAAAIAVCN2Nnd0hDbvH+3rJ01JxToOX4Iyw68Zh3tbuF+ZvUkkZrd0AAAAAmgZCd1PQ+yIppYtUnC0tf8GSEi7un6lWCVHalVusd7/ZZkkNAAAAANDQCN1Ngc0uDZvqfb3sOanwQIOXENDaPW8jrd0AAAAAmgRCd1PR4zypZS+pJFda9qwlJVzSP1Op8U7tyCnWB6u3W1IDAAAAADQkQndTYbNVtHYvf1Eq2NfgJUQ57LphqLe1+7l5G1Va5mnwGgAAAACgIRG6m5JuZ0ppx0muAumTWyyZQmzcgDZqEe/U9uwiffgt93YDAAAAaNwI3U2JYUgjpkuGTVr3qfTyUGnnjw1aQpTDrutP7SBJenbeRrnctHYDAAAAaLwI3U1Nh2HShE+k+HRp/0bpleHSipcl02ywEsYPaKuUuEhtO1ik2d9xbzcAAACAxovQ3RS1GyzdsFjqMlpyl0qf3ym9M77BRjWPjrTruvLW7ufmbVQZrd0AAAAAGilCd1MV21y67B1p9GOSPVJa/1/pxcHS70sb5PSXn9xWzWMj9fv+Qv3n+x0Nck4AAAAAaGiE7qbMMKSTb5CumSMld5Ryt0szzpQWPC55gjuPdkxkhK4tb+1+9PN1Wv17w88dDgAAAADBRuiGlH6cdP0Cqc+lkumR5v1VevNcKTe4LdBXnNxWXVvGa19+iS55ablmLNksswHvLQcAAACAYCN0w8sZL13wknTei5IjVtqySHphkPTrl0E7ZawzQh9MOkVn9k5TmcfUtE9+0S3vfK/C0rKgnRMAAAAAGhKhG4GOu0y6fqHUqo9UdECaebH0xVSprCQop4tzRujZccfrvjO7y24z9PEPO3T+c0v1296Gn0McAAAAAOoboRvVpXSS/viVNOBG7/vlz0uvjpD2bwrK6QzD0B+HdNDMPw5Qi3in1u/O07nPLtEXP+0KyvkAAAAAoKEQulGzCKc05lHp0rel6CRp5w/SS6dKP8wK2ikHdGiu/04ZrP7tkpRXUqYb/r1aj36+jinFAAAAAIQtQjcOr9tY6YYlUttBUmm+NPs6afaNUklwun+nJkRp5rUn65rB7SVJLy7YpCteXal9+cHp3g4AAAAAwUToxpE1ay1N+EQaNlUybNIPM6WXh0o7fwzK6Rx2m+4/q4eeHXe8YiLtWvbbfp31z8X6duvBoJwPAAAAAIKF0I3asdmlYfdIEz6V4tOl/RulV4ZLK16WgjTN11l90vXR5EHq0CJWu3KLdclLy/Tmsi1MKwYAAAAgbBC6UTftBkk3LpG6jJHcpdLnd0rvjJcKDwTldJ1bxuujyYM0plcrudymHvjoZ93+7g8qKnUH5XwAAAAAUJ8I3ai7mGTpsrel0Y9J9khp/X+lFwdLvy8Nyunioxx6fvwJ+r+x3mnFZn+3Xec/v0Rb9hUE5XwAAAAAUF8I3Tg6hiGdfIN3arHkjlLudmnGmdKCxyVP/bdCG4aha0/toLf+OEApcZFatytPZz+7WHN+2V3v5wIAAACA+kLoxrFJ6ytdv1DqO04yPdK8v0pvnivl7gjK6U7u0Fz/vXmI+rVNUl5xma598xv97ct1cnu4zxsAAABA6CF049g546TzX5DOf0lyxEpbFkkvDJLWfxGU07VMiNLb156siae0kyQ9N2+TJry2UvuZVgwAAABAiCF0o/70vdTb6t2qj1R0QHr7EumLqVJZ/YfhyAibpp3TU09fepyiHXYt3rhPZz+zWN9nZdf7uQAAAADgaBG6Ub9SOnnv8z55kvf98uelV0dI+zcF5XTnHtdaH900SB1SYrUjp1gXvbhU/17+O9OKAQAAAAgJhG7UvwinNPoR6bJZUnSytPMH6aVTpR9mBeV0XVrG66ObBmlUz5ZyuU3d95+f9Kf3mFYMAAAAgPUI3QierqO9c3q3HSyV5kuzr5Nm3yiV5Nf7qeKjHHrx8n6aOqabbIb04bfbdcELS/X7fqYVAwAAAGAdQjeCKyFdmvCxNOxeybBJP8yUXh4q7fyx3k9lGIauH9pR/y6fVmztzlyd9cxizV3LtGIAAAAArEHoRvDZ7NKwu6UJn0oJraX9G6VXhksrXpaCcO/1KR1T9OmUITqhTaLyist0zRvf6In/rWdaMQAAAAANjtCNhtNukHTDYqnrWMldKn1+p/TOeKnwQL2fqlWzKL1z3UBNGNhWkvTM1xs18fWVOlBQWu/nAgAAAIBDIXSjYcUkS5fOlMY8LtkjpfX/lZ7sLs26Qvr5P1JpYb2dKjLCpunn9tJTl3inFVu0wTut2A9MKwYAAACggRC60fAMQxpwvfTHuVLLXlJZsbT2Y+m9CdLfOknvXyOt+6ze5vc+7/jWmj35FLVrHqPt2UW66MVlenvlVqYVAwAAABB0hG5YJ62Pt7v59QulQbdKiW0kV4H00/vSO5dJf+vsHe18w1eS23VMp+rWKkEfTxmsET1aqtTt0dQP1+iu939UsYtpxQAAAAAED6Eb1jIMKa2vNGK6dMuP3tbvkydL8elSSY53tPO3LpT+3kX6+GbptwWS5+iCckKUQy9d3k93je4qmyG9t3qbLnxhqbbur78u7QAAAABQGaEbocMwpIwTpdEPS7f9LF31udT/Wim2hVR0QPr2DenNc6Qnukn/vUP6fZnk8dTpFDaboUnDOun/XTNAzWMj9fOOXJ31zCLNW7cnSB8KAAAAQFNG6EZostmktqdIZ/5dun2ddOVH0gkTpOgkqWCPtOpf0uujpad6SV/+n7RtdZ2mHxvUKUWf3jxYx2UmKre4TFfNWKUn5/zKtGIAAAAA6hWhG6HPHiF1GCad80/pjg3S+PelvpdJzgQpd7u07FnpldOlp/tKX02Tdv5YqwCe1ixas64/WVec7J1W7J9zN+jK11Zozbac4H4eAAAAAE1GhNUFAHVid0idR3gfrmJp01zppw+k9Z9L2b9Li//hfTTvJPW6UOp5gZTa7ZCHc0bY9Zfzeun4Nom6d/YaLdm4X2c/u1hDu7TQlNM76cR2yQ344QAAAAA0NoRuhC9HlNTtTO+jtFDa8KU3gP/6P2n/RmnBY95Hag+p1wXeAN68Y42HuuCEDB2Xmahnvt6oj77frgW/7tWCX/dqQPtkTTm9swZ1ai7DMBr4AwIAAAAId4RuNA6RMVLP872P4lxvy/fPH0ob50p7fpG+/kX6+iHvSOm9LvRul9gm4BAdWsTpH5ccp1vP6KwXF2zS+6u3acXmA1rx6gr1zUzUlNM6aXj3VMI3AAAAgFojdKPxiUqQ+l7ifRQdlNZ+6g3gvy2Qdv7gfcx5QMo4ydsC3uM8KSHNv3vb5rF65II+mnJ6Z7288De9vXKrfsjK1h/f/EbdWsVr8mmdNLZ3muw2wjcAAACAw2MgNTRu0UnSCVdIV8yW7vhVOvNJqd0QSYa0baX0xT3Sk92l18+UVr0i5VdMHZaeGK1p5/TU4rtP1w1DOyo20q51u/I05e3vNOLJBXrvmyy53HWbsgwAAABA00JLN5qO2BSp/zXeR+5O6ZePvC3gWSuk3xd7H//9k9S8s9R2oNTmFKntQLVIbKt7xnTTjUM7asbSLXptyWb9tq9Ad77/o576aoNuGNZRF/XLUJTDbvUnBAAAABBiCN1omhLSpJNv8D6ys6SfZ3sD+I7vpP0bvI9v3/RuG58utR2oZm0G6pbep+iawcP01oos/WvRZm3PLtL9//lJz8zdoGuHdNC4AW0U6+SfFQAAAAAvS7uXL1y4UGeffbbS09NlGIb+85//WFkOmqrETGnQzdJ186W7NkuXvi2dcrOU0V+yRUh5O7yjon92h/TCKYp7qpOu3zZVy4b8qOdPdalNgl178kr018/WavBjX+uZuRuUU+Sy+lMBAAAACAGWNskVFBSob9++uvrqq3XBBRdYWQrgFZMsdRvrfUjeqci2fyP9vkzaulTKWiUV50gbvpRjw5caK2lMRJT2pvfSF3ntNaego16ck6uXF/6mK09pq6sHtVfzOKelHwkAAACAdSwN3WPGjNGYMWOsLAE4vMgYqf2p3ockucukXT9IW5dLvy+Vti6TUbhfqQe+0ZX6RldGSm7Z9IunjVYt6qbpi3uo7QnDdfnwE9UyIcrazwIAAACgwYXVzaclJSUqKSnxv8/NzZUkuVwuuVyh253XV1so14g6SO3jfZx4nWSa0v6NMrKWyZa1XMbW5bLnbFVv2xb1tm2R9IX0w5P67fs0rUo+QW2PP11J3YZKiW2lMJrvm2sY4YzrF+GM6xfhjOsX4exw129dr2nDNE2zXqo6RoZhaPbs2TrvvPMOuc20adM0ffr0astnzpypmJiYIFYH1F5U6QE1z1+v5Pz1isv9VamubdW2KYhI0sG4LjoQ10X7Y7sqNzpDMpjBDwAAAAh1hYWFGjdunHJycpSQkHDE7cMqdNfU0p2Zmal9+/bV6sNaxeVyac6cORoxYoQcDofV5aChFWVrw+q52rT6K6Xnfq/exm+KNNwBm5jOBJkZJ8lsM1Bm5sky046TIkLnXnCuYYQzrl+EM65fhDOuX4Szw12/ubm5SklJqXXoDqvu5U6nU05n9SDicDjC4h9yuNSJeuZooR6nXaoep12qH7Kyddvcn7T/12Xqb6xTf9t6nRSxUVEluTI2fSVt+sq7T0SUlNZXapYhxaeVP1oFPkc2fO8OrmGEM65fhDOuX4Qzrl+Es5qu37pez2EVuoFw1zczUc9NHKx1u/rouXmb9OyPO2RzudXd+F2XpGZpbLMtSt63WkbhPilrhfdxKM5m3vnGq4Zx/3OaFNdSiohsuA8IAAAAIICloTs/P18bN270v9+8ebO+//57JScnq02bNhZWBgRXt1YJeuay43XbGZ31wvxNmv1dhO7f3UH37x6qfm1u0Z3D7BoQvV1G/i4pb6eUt6v8sVPK3Sm5CqSSHGlvjrR33eFPFpNSKYyXB/KEKq3nsS0km71hPjwAAADQhFgaur/55huddtpp/ve33367JGnChAmaMWOGRVUBDadDizj97aK+uuWMznppwW+a9U2WVm/N1qVbpW6tWujkDt3UIz1BPXslqHNqvCIjbN4R00vyKkJ4wPOOwPfuUqlwn/exe82hCzFs3lbx+FZSfHr1VvPoFDldOZLHLYnuYQAAAEBtWRq6hw0bphAZxw2wVEZSjP5yXi9NOb2T/rXoN721YqvW7crTul15/m0i7TZ1bhmnnukJ6pneTL1ap6hb6w6KdR7in7FpSkUHywN4eQjP3Vk9qOfvlkx3xXb6rtqhHJJGSzJ/ulmKaS7FpXpbx2NblL9OkWLLl8W1qHjtYG5yAAAANG3c0w2EkNSEKP3fmT00aVgnzVu/Rz/vyNXPO3L0845c5RWXlb/PleSdhswwpPYpseqZ3qw8jHsDeXJspHdlTLL30bLnoU/qcUsF+6q3kld6NnN3SoX7ZcisaDmvDWdCpUCeUh7QK79uUfE+qllYzV0OAAAA1AahGwhBSbGRuuCEDF1wgve9aZradrDIH8B9YXx3bol+21ug3/YW6JMfdvj3T2sWpZ7pCepRKYy3ToyWUVOotdml+JbexyGUuVz67L+fauzQk+QozZby90gFe72P/D3e0F6wJ/C1u1QqyfU+Dvx25A9td5aH8JTAlnR/a3ql19HJkp0/XwAAAAh9/F8rEAYMw1Bmcowyk2M0uleaf/nevBJ/EP+lPIhv2V+onTnF2plTrK/W7vFvmxjjUI80bwDv1dobxtunxMluq2XrsmHzBl5H68O3nEvl953nSvnlwbxqIC/YW76ufFlJruQukXK3eR+14WwmxSR5A3hMcvlz8/LXSZWWVXp2xNCaDgAAgAZF6AbCWIt4p4Z1TdWwrqn+ZXnFLq3dmRfQKr5hd56yC11aumm/lm7a79822mFXt7R4f7f0nukJ6tIyXlGOYxzJ3DC83cWjmkkpnY68vauoUmt5paBesK96q3rhfkmmd/T2khzp4Jba12V3BobwmoJ51eeoRMlmO8ofBAAAAJo6QjfQyMRHOXRS+2Sd1D7Zv6ykzK1fd+VXCuI5WrszT0Uut77bmq3vtmb7t42wGeqUGucdNd0XxFtEB7doR7SUmOl9HInHLRVlS0UHpMID5c/7K732PR8MfO8u9bam+weMqy1Dik6s0pKeXNGiHp3oDebOBCkqofy5mfd1ZBwt6wAAAE0coRtoApwRdvXOaKbeGc38y9weU5v3FejnHTnlXdO9Yfxgocs/cvqH3273b58Yade7e1arc8t4dUyNU8cWseqUGqcWcc6a7xUPFptdim3ufdSWaUqlBd5w7g/iB2sI6pVD/EGpNE9S+SjwRQelA5vqVqthk5zx3q7wviDuC+dRzaoEdd+yZoHh3RFNcAcAAAhjhG6gibKXt2h3So3Tuce1luQdsG1nTrE/gP+0PVe/7MjRjpxiZZcaWrJpv5ZU6p4uSfFREeqUGqeOLeICnjOTohVhD5Fu2YYhOeO8j6S2td+vrLQ8cNcUzMsfxdlScY73vvTi3Ipnj0syPd51xTlSzlHWbouooRW92aHDe2ScFBlb/eGIpZs8AACABQjdAPwMw1B6YrTSE6M1okfFaOZ7cwr11idzlNqpj7YcKNbGPfnatDdfWQcKlVdcVq2LuuSdV7xdSky1MN6hRaxiIsPkT09E5BFHdq+RaXrvUw8I4jk1hPOcwKBekhO4zPRInjJvyC86cOyfxxFTKYhXCueOmCph/VCvY6svt0fSEg8AAHAYYfJ/vgCslBjjUPt4aWy/DDkcDv/yYpdbW/YXeEP4ngJt3JuvTXvy9du+fBW7PPp1d75+3Z1f7XitE6PVobx7euVQnhIX2bBd1YPFMKTIGO8jvtXRHcPXJb5aOM85RGAvf3YVePfzP/K94V2SXIXeR8He+vustogaQrzvOUqKiK7hufwREVXp+VDbRnm/FCDcAwCAMEXoBnDUohx2dWuVoG6tEgKWezymtmcX+UP4pr0VofxAQam2Zxdpe3aRFm3YF7Bfs2iH/17xyoE8Iymm9lObNRaVu8QnpB/9cUxTKiuuCOClBVJpYaXXBYd5Xf7eVVh9XVmx9/iesopW/KAyagjqNQR3R0y1dTabQx32/Cbj272SM0aKcHpHso+I8vZmsDu9y3yPyusiorxfLBD4AQDAUSJ0A6h3NlvFvOKnVZrOTJIOFJSWh/B8fzf1jXvzte1gkXKKXPp2a7a+rdpVPcKmDimx6tgiTh1T49SueYwykmKUmRytlvFRsjW1QF4XhlHRshybUn/HdZdVaVWvKawXS2VFtXg+zDpfK73Mipb6orqVapfUW5K2//soP6wRGMJrDOmHeh/lbaUP2D+y0sNR5bXjEMtreG07xqn9AABAgyB0A2hQybGRSo5NVv92yQHLi11u/ba3wBvCfWF8T75+21eg0jKPf0T1qiLtNrVOilZGUrQ36CfFVHodreTYRtJlPdTYIyR7+aBuwWKakttVPYy7Cr0t7a6iQzwHBndPSYF2ZG1RemqybB6XVFbifbhLKl5Xfe9xVS7Ee7yyIh39iHhBYNhqCOO1COt2h2RzBAZ9W4T34V8XUf5c0/v62pYeBACApoHQDSAkRDns6pGeoB7pgV3V3R5T2w8WBYTxrQcKlXWwUDuyi1Xq9mjzvgJt3ldQ43FjIu3KLG8VzwgI5N5l8VGOGvdDCDCM8tbhyGMK926XS6s/+0wtx46VzVHL37fHUx7Ci72j2JcVe+d6D3h/mNBe4/aVnt2uKs+1eO0pC6zR9JQfv/iofzaWs9UQ0P0Pe+B7+xHWB7x3HGF9pff+c1dZb9jLl9nLX0dUeW+v5XYRVfapuh9fPgBAY0foBhDS7DZDbZrHqE3zGJ3WLbCrepnbo505xco6WKhtB4u07UChsg4WKas8lO/OLVFhqVvrd+dp/e7qreSSd5C4qq3jGZVazKMcdOFtkmw2yVbeLT9UeDzeFvjDBvY6hHjfa98xPWUVzx6X9xaCautqel/TdjUcQ2YNn6nM+yir4z0DjY5RJZxHlF+DEYowbBpZ4lLEb/9Xvt5WEdgNu3e7ast829mqLLN7A361Zbby41RdZg88RrXXlc5defuAc9dl/aGOV5vzGYHrqj2Mmmuoug0ABAGhG0DYirDb/PeO16TY5db27CJtqxTEtx0oUtbBQmUdKNTBQpeyC13KLszRmu01dxtuEe9UZpXWcW8gj1FaYpQcoTIXORo/m02yld8rHo487iMHd4+7PIi7KwL5IR9Vt6lpn0rL3K5a7ON7X76tb9o+33LTXf7aXf66rOb3lbfz7ecfn6AmZvk5XdXWGJKiJSmnHqYNxJEdMrTbKn1hcZhQ73vIqGGdUcO6KvvKqHKsQx236vOhjmscopaaaqypvpqW6Qifs+Jz2Dym2u772TuQZYSjSj2Gqn1mqWLdYbczKmqpvN0R96n8MzFq2LfKtgHrdYT1h9q/6usa1h9xX4Q7QjeARivKYfcOvtYirsb1+SVl2nawUFkHKkJ51oEibStvOc8vKdPevBLtzSupNrib5G2FT413KjUhyvsc71RL3+sEp1Ljo5Sa4FTzWGfTG30dqMrXtVpRVldiDdOsIayXHSLYe/yvXaXFWrp4oQYNHKgIuy0w1PvCvMcTuMz3hYHpqWGZu8r2VV5XW1Zp+6rr/eeuab2nSg212cdd6edUaf0h9ynf3jSrb1/5UaffU/lxUS/sko6TpCxr62gc6hDUA55r2PeI+xxm38PVUutjHOE5qpk0blawf6ANitANoMmKc0bUOOWZJJmmqexClz+Ie7uwV35dpNIyb/f2nTmHv6fWbjOUEhfpDeGVQ3p5MG9Z/pwSF+n9n2oAjY9heO9LV4SkOvRWcLmUHbNNZut+Um3HJEAgXyiv6QsB/8M8dGgPCP2He5hVwr5Zw3rzMOsrH8M8zDGqfKFQ4+eour9qOG9NtZiHOHdN2x75mB53mXbv2qWWLVNlM4waPltNr1WxXdXjVtunvI7K2x1uH/96s+bzVztO5e3quE/9X8jlxw7S4UNJdLLVFdQ7QjcA1MAwDCXFRiopNlJ9MhKrrfd4TO3NL9HOnGLtyS3W7rwS7c0t1p68Eu0uf96TV6J9+SVye0ztzi3R7tySI5xTah7r9AfyluUt5YFBPUot4pyKjCCcA0CtGIb896ujQbldLq387DONrctAlo2FeZhQXi20V12nmtcddp+q645wjMPVILPSlxl1qKO+jmOPDM7vxEKEbgA4CjaboZYJUWqZcPiusmVuj/YXlGpPbon25BVrd/nznrwS7fGF89wS7S0P5/vyvUH9l52HP39ybKRS451qUalLe4t4p5JjI5UYE6mkGIeSYrxfGsRG2pk2DQCAhhRwPzZf+DR1hG4ACKIIu61SOG92yO3cHlMHCkoDA3luSUDL+d48b2B3ub3bHigorXHu8qocdiMwiMdEKinWocSYSCXHRCrRH9Ar1jeLdsjGfegAAADHjNANACHAbjPUory1uudhtvN4TGUXubzhPDcwkO/NK9HBwtLyUdm9obykzCOX2/Svry3DkBKjvSE8sVKreVKMozzARyo5tuK1bznd3gEAAAIRugEgjNhshpJjI5UcG6lurY68fVGpuzyIl+pggUsHC0vLA3nF64OFLv822QUu5ZWUyTRVvrz6FEaHE+eM8If0+KgIxTkjFB/lqPQ6QnFR5cucvtfl65wOxUVFMNI7AABoVAjdANCIRUfaFR0ZrfTE6FrvU1rmUXZRqbILXTpYUCWU+5dVLM8ub1n3mN5p2LxTsRUddc0xkfZKAd0bzn3B/JCBvXy5b5u4yAi6xwMAgJBA6AYABIiMsJVPb1b7+ZQ9HlO5xd6W8QMFpcopKlVecZnyir0hPK/YpfziMuWVlC+rvLx8WUmZd8TWwlK3Ckvd2lOH7vA1iXOWB3WnXa4iu97Z/Y1inRGKiYxQTKS94tlpV2xkhKIjvc/edXbFOiuWeZ/tTOkGAADqjNANADhmNpt3sLbEmEi1T4k9qmOUlnm8LeXFZcqtFMbzS1zly8r86yuHdV+w94V4l9s7VYpvmZeh3/MPHPPnjIyweQN5pSDuD+vOCMU4vCHeF+pjfeG+0rJoh11RDruiHLbyZ+/rSLuNUeYBAGiECN0AgJAQGWFTcoT3fvWjZZqmSso8FUG8uEwHC4q0cOlK9ehznErc5S3pJWUqKHWrqNT37FZBaVl5K3uZCku8re2+ZW6PN8iXlnm83e/reK97bRiGFBVRNYyXv6+2vNLrgHVV9o04xD6EfAAAGgyhGwDQaBiG4Q+WLeKdkiSXK0YH15ka2ydNDoejzsc0TVOlbk95MPcGdl8gr7qs0B/cva8D13n3KS51q7jMo2KXW8Uut8rzvExTKnK5VeRyS6r/UF+VL+RHRtgUGWGT0/9s9793VloeaQ9cV3nbwGW+R+C2VY/tfyb8AwAaOUI3AACHYRiGnBF2OSPsSoyp32ObpimX21RxmTeAF5d6Kl67KoJ5cZmnPKxXXVe+vX9dpX3K15W4PCryLztUyLeWN9Db5IiwyWE35LB7w7jDbpMjwvC/jowoX1bDNgHv/dsalfYpf+/fp8p7e8WXCxF2QxF2Qw6bd7sIm/f4jKwPADgahG4AACxiGIYiI7zBMCGq7q3wdVU15Je4PCop86ikzO3vOl9S+dnt3abUXbGs8va+bUtrWFZSZXmp2+M9X/mxKit1e9fr2MbOCzrDkBy28lBeHsS9r70BPsJuC1ju39ZuU2T5dhHlXxhE2LzLHQH7Vz+WTabW7TZUsHqbIh0OOeyG7Dbv+e02W/mz4T9exTrDX6dvuwj/voHbOew22QzR4wAAgoTQDQBAE9HQIf9QfF32A0O7R67yQO5ye+Rym973bo9cZVXeV1pWGrBPxTJXWZX3/v0q3pfWtE2Z7xxmDXX7viBo6J+YXe/+9kvQz1I5wNttFcE/wmbIbq8I63ajItTbjIrt7VX2r3iUf4FgVBzLf4xq23qX2Sp9sWA3JLvdJnsN57KXH9f7WhWvjYpj2CrVXNO2NiPwvL59K44j//58MQHgaBC6AQBAg6rcZT9UmaYpt8dUmccb9svcplwe73Pl1y63x7+Nb7syjze0B772yOXxPpeVh/yq62s6V2mZW9t37FRKaqrcpiF3+XJfbRXPvuP5lnkq1lVa7vJ4ZFb/PkGSVFa+fYh3OLCUYVQEcHuVgG4zDNkMVbz2BXvD8O7n36YiyNt8ob/qNrbyYxneoO/7kqBiewVuV+nY3i8HVP11eX2G/8uGwNf+2gz5z1P9vfxfPlSur3INlT+Hx+3Wxlzpm98PKtIRUb5txfmMKuc1jMBaDP+567B9lePzRQlCAaEbAACgCsMo755tl6Ic1n054HK59Nln2zV27AlHNRBgTTyeQ4Tz8tAfEOjdgdu53KY8pnd95eP4tvGYFfu4zYpj+PZxV/qiwFPpCwO3R94vDjxmxTHMmratcj6P/Nt5zIp1lWt0m6Z3O//r8mNXOofH/3z4n51pSmXlx0ZtReiZn1dZdvbKQdwX0g1VCfI2Q4Z876uH/KrHCHivSu/LvxwxVP0LARmq9gWBYfjOqxqWVRxXRsVxK9fg+xwVdfr2qbyu5m0Dj1nzuQ15fzaquqzK9sZh6ve9rny+mrY3Kq13Omw6vVvLhr5UgorQDQAA0ITYbIYi/YPChW5vAyuYpjd4+8O9L4xXDeyewIDv9sgf3E3T+0WAp3x73/FMs/wYpsqXVwR9j2kG7l++3mNW2b/a9go8T/lrU951Ne3jqVJD1dduM/BcNR7Do8BjVfmsHtP7Oco8HuXl5SsmNlam5N/X93M2VXGOyscx/eeS/1iVz1e332n5FzPed/V/0aDeJcU49N0DI60uo14RugEAAACVd7cu7x6NY+ftqfGZxo4dXG89NaQqId2sIaRX+VLArPKFglktzHu3qfzFgG+974sBT/m+3m2rn/tQz5XPH/glg28bBdakSuf2vff4lldsW3Gsitr8xytfp2rnremYVc5V6ThS1c/jPZYCaq3Yt/rPtsoxKm0vVf95+Y4Rb+GYI8FC6AYAAAAQNvxfjogvRxAebFYXAAAAAABAY0XoBgAAAAAgSAjdAAAAAAAECaEbAAAAAIAgIXQDAAAAABAkhG4AAAAAAIKE0A0AAAAAQJAQugEAAAAACBJCNwAAAAAAQULoBgAAAAAgSAjdAAAAAAAECaEbAAAAAIAgIXQDAAAAABAkhG4AAAAAAIKE0A0AAAAAQJAQugEAAAAACBJCNwAAAAAAQULoBgAAAAAgSCKsLuBYmKYpScrNzbW4ksNzuVwqLCxUbm6uHA6H1eUAdcY1jHDG9YtwxvWLcMb1i3B2uOvXlz99efRIwjp05+XlSZIyMzMtrgQAAAAA0JTk5eWpWbNmR9zOMGsbz0OQx+PRjh07FB8fL8MwrC7nkHJzc5WZmamsrCwlJCRYXQ5QZ1zDCGdcvwhnXL8IZ1y/CGeHu35N01ReXp7S09Nlsx35ju2wbum22WzKyMiwuoxaS0hI4A8OwhrXMMIZ1y/CGdcvwhnXL8LZoa7f2rRw+zCQGgAAAAAAQULoBgAAAAAgSAjdDcDpdOrBBx+U0+m0uhTgqHANI5xx/SKccf0inHH9IpzV5/Ub1gOpAQAAAAAQymjpBgAAAAAgSAjdAAAAAAAECaEbAAAAAIAgIXQDAAAAABAkhO4G8Nxzz6ldu3aKiorSgAEDtHLlSqtLAo5o2rRpMgwj4NGtWzerywJqtHDhQp199tlKT0+XYRj6z3/+E7DeNE098MADSktLU3R0tM444wxt2LDBmmKBGhzpGp44cWK1v8mjR4+2pligkkceeUT9+/dXfHy8UlNTdd5552n9+vUB2xQXF2vy5Mlq3ry54uLidOGFF2r37t0WVQxUqM31O2zYsGp/f2+44YY6nYfQHWSzZs3S7bffrgcffFDffvut+vbtq1GjRmnPnj1WlwYcUc+ePbVz507/Y/HixVaXBNSooKBAffv21XPPPVfj+scff1z//Oc/9eKLL2rFihWKjY3VqFGjVFxc3MCVAjU70jUsSaNHjw74m/z22283YIVAzRYsWKDJkydr+fLlmjNnjlwul0aOHKmCggL/Nrfddps++eQTvffee1qwYIF27NihCy64wMKqAa/aXL+SdO211wb8/X388cfrdB6mDAuyAQMGqH///nr22WclSR6PR5mZmZoyZYruuecei6sDDm3atGn6z3/+o++//97qUoA6MQxDs2fP1nnnnSfJ28qdnp6uP/3pT7rjjjskSTk5OWrZsqVmzJihSy+91MJqgeqqXsOSt6U7Ozu7Wgs4EGr27t2r1NRULViwQKeeeqpycnLUokULzZw5U3/4wx8kSevWrVP37t21bNkynXzyyRZXDFSoev1K3pbu4447Tk899dRRH5eW7iAqLS3V6tWrdcYZZ/iX2Ww2nXHGGVq2bJmFlQG1s2HDBqWnp6tDhw4aP368tm7danVJQJ1t3rxZu3btCvhb3KxZMw0YMIC/xQgr8+fPV2pqqrp27aobb7xR+/fvt7okoJqcnBxJUnJysiRp9erVcrlcAX+Du3XrpjZt2vA3GCGn6vXr89ZbbyklJUW9evXS1KlTVVhYWKfjRtRbhahm3759crvdatmyZcDyli1bat26dRZVBdTOgAEDNGPGDHXt2lU7d+7U9OnTNWTIEP3000+Kj4+3ujyg1nbt2iVJNf4t9q0DQt3o0aN1wQUXqH379tq0aZPuvfdejRkzRsuWLZPdbre6PECSt0fnrbfeqkGDBqlXr16SvH+DIyMjlZiYGLAtf4MRamq6fiVp3Lhxatu2rdLT0/Xjjz/q7rvv1vr16/Xhhx/W+tiEbgA1GjNmjP91nz59NGDAALVt21bvvvuurrnmGgsrA4Cmp/JtEL1791afPn3UsWNHzZ8/X8OHD7ewMqDC5MmT9dNPPzEGDMLSoa7f6667zv+6d+/eSktL0/Dhw7Vp0yZ17NixVseme3kQpaSkyG63Vxudcffu3WrVqpVFVQFHJzExUV26dNHGjRutLgWoE9/fW/4WozHp0KGDUlJS+JuMkHHTTTfp008/1bx585SRkeFf3qpVK5WWlio7Oztge/4GI5Qc6vqtyYABAySpTn9/Cd1BFBkZqX79+mnu3Ln+ZR6PR3PnztXAgQMtrAyou/z8fG3atElpaWlWlwLUSfv27dWqVauAv8W5ublasWIFf4sRtrZt26b9+/fzNxmWM01TN910k2bPnq2vv/5a7du3D1jfr18/ORyOgL/B69ev19atW/kbDMsd6fqtiW+Q4br8/aV7eZDdfvvtmjBhgk488USddNJJeuqpp1RQUKCrrrrK6tKAw7rjjjt09tlnq23bttqxY4cefPBB2e12XXbZZVaXBlSTn58f8I3z5s2b9f333ys5OVlt2rTRrbfeqoceekidO3dW+/btdf/99ys9PT1gdGjASoe7hpOTkzV9+nRdeOGFatWqlTZt2qS77rpLnTp10qhRoyysGvB2yZ05c6Y++ugjxcfH++/TbtasmaKjo9WsWTNdc801uv3225WcnKyEhARNmTJFAwcOZORyWO5I1++mTZs0c+ZMjR07Vs2bN9ePP/6o2267Taeeeqr69OlT+xOZCLpnnnnGbNOmjRkZGWmedNJJ5vLly60uCTiiSy65xExLSzMjIyPN1q1bm5dccom5ceNGq8sCajRv3jxTUrXHhAkTTNM0TY/HY95///1my5YtTafTaQ4fPtxcv369tUUDlRzuGi4sLDRHjhxptmjRwnQ4HGbbtm3Na6+91ty1a5fVZQM1XreSzNdff92/TVFRkTlp0iQzKSnJjImJMc8//3xz586d1hUNlDvS9bt161bz1FNPNZOTk02n02l26tTJvPPOO82cnJw6nYd5ugEAAAAACBLu6QYAAAAAIEgI3QAAAAAABAmhGwAAAACAICF0AwAAAAAQJIRuAAAAAACChNANAAAAAECQELoBAAAAAAgSQjcAAAAAAEFC6AYAALVmGIb+85//WF0GAABhg9ANAECYmDhxogzDqPYYPXq01aUBAIBDiLC6AAAAUHujR4/W66+/HrDM6XRaVA0AADgSWroBAAgjTqdTrVq1CngkJSVJ8nb9fuGFFzRmzBhFR0erQ4cOev/99wP2X7NmjU4//XRFR0erefPmuu6665Sfnx+wzWuvvaaePXvK6XQqLS1NN910U8D6ffv26fzzz1dMTIw6d+6sjz/+OLgfGgCAMEboBgCgEbn//vt14YUX6ocfftD48eN16aWXau3atZKkgoICjRo1SklJSVq1apXee+89ffXVVwGh+oUXXtDkyZN13XXXac2aNfr444/VqVOngHNMnz5dF198sX788UeNHTtW48eP14EDBxr0cwIAEC4M0zRNq4sAAABHNnHiRP373/9WVFRUwPJ7771X9957rwzD0A033KAXXnjBv+7kk0/WCSecoOeff17/+te/dPfddysrK0uxsbGSpM8++0xnn322duzYoZYtW6p169a66qqr9NBDD9VYg2EYuu+++/SXv/xFkjfIx8XF6fPPP+fecgAAasA93QAAhJHTTjstIFRLUnJysv/1wIEDA9YNHDhQ33//vSRp7dq16tu3rz9wS9KgQYPk8Xi0fv16GYahHTt2aPjw4YetoU+fPv7XsbGxSkhI0J49e472IwEA0KgRugEACCOxsbHVunvXl+jo6Fpt53A4At4bhiGPxxOMkgAACHvc0w0AQCOyfPnyau+7d+8uSerevbt++OEHFRQU+NcvWbJENptNXbt2VXx8vNq1a6e5c+c2aM0AADRmtHQDABBGSkpKtGvXroBlERERSklJkSS99957OvHEEzV48GC99dZbWrlypV599VVJ0vjx4/Xggw9qwoQJmjZtmvbu3aspU6boiiuuUMuWLSVJ06ZN0w033KDU1FSNGTNGeXl5WrJkiaZMmdKwHxQAgEaC0A0AQBj54osvlJaWFrCsa9euWrdunSTvyOLvvPOOJk2apLS0NL399tvq0aOHJCkmJkZffvmlbrnlFvXv318xMTG68MIL9eSTT/qPNWHCBBUXF+sf//iH7rjjDqWkpOgPf/hDw31AAAAaGUYvBwCgkTAMQ7Nnz9Z5551ndSkAAKAc93QDAAAAABAkhG4AAAAAAIKEe7oBAGgkuGMMAIDQQ0s3AAAAAABBQugGAAAAACBICN0AAAAAAAQJoRsAAAAAgCAhdAMAAAAAECSEbgAAAAAAgoTQDQAAAABAkBC6AQAAAAAIkv8P+JHkUrHIp5IAAAAASUVORK5CYII=\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "dot_network = DLRM(\n",
+        "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
+        "    embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        ")\n",
+        "rmse, dot_network_num_params = train_and_evaluate(\n",
+        "    learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
+        "    epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
+        "    train_data=train_ds,\n",
+        "    test_data=test_ds,\n",
+        "    model= dot_network,\n",
+        "    plot_metrics=True\n",
+        ")\n",
+        "print_stats(\n",
+        "    rmse_list=[rmse],\n",
+        "    num_params=dot_network_num_params,\n",
+        "    model_name=\"Dot Network\",\n",
+        ")\n"
       ]
     },
     {
@@ -764,6 +756,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R8WPVVn-Jq81"
+      },
+      "outputs": [],
       "source": [
         "def get_dot_interaction_matrix(model, feature_names):\n",
         "    # Extract the trained embedding weights from the model\n",
@@ -782,26 +779,11 @@
         "            interaction_matrix[i, j] = interaction_strength\n",
         "\n",
         "    return interaction_matrix"
-      ],
-      "metadata": {
-        "id": "R8WPVVn-Jq81"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# Get the list of feature names in the correct order\n",
-        "feature_names = list(vocabularies.keys())\n",
-        "\n",
-        "# Calculate the interaction matrix\n",
-        "interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)\n",
-        "\n",
-        "# Visualize the matrix as a heatmap\n",
-        "print(\"\\nVisualizing the feature interaction strengths:\")\n",
-        "visualize_layer(interaction_matrix, feature_names)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -810,35 +792,45 @@
         "id": "NH-VtGrMJtAt",
         "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "Visualizing the feature interaction strengths:\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "text/plain": [
               "<Figure size 900x900 with 0 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 480x480 with 2 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "# Get the list of feature names in the correct order\n",
+        "feature_names = list(vocabularies.keys())\n",
+        "\n",
+        "# Calculate the interaction matrix\n",
+        "interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)\n",
+        "\n",
+        "# Visualize the matrix as a heatmap\n",
+        "print(\"\\nVisualizing the feature interaction strengths:\")\n",
+        "visualize_layer(interaction_matrix, feature_names)"
       ]
     },
     {
@@ -876,6 +868,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8mskfVqa-uGx"
+      },
+      "outputs": [],
       "source": [
         "def plot_rmse(rmse_list, model_name):\n",
         "  plt.figure()\n",
@@ -886,21 +883,18 @@
         "  plt.show()\n",
         "\n",
         "plot_rmse(dlrm_rmse_list, \"Dot Network\")"
-      ],
-      "metadata": {
-        "id": "8mskfVqa-uGx"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     }
   ],
   "metadata": {
+    "accelerator": "GPU",
     "colab": {
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+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_93911ffe44c94b149031cce6606355db",
+            "placeholder": "​",
+            "style": "IPY_MODEL_423b08111fd444b5b8739d6bb5c64393",
+            "value": " 1/1 [00:00&lt;00:00,  1.60 url/s]"
+          }
+        },
+        "f0b8e85a1e854efa8a43b9ada4109915": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "FloatProgressModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_cf2d1c56ac624e7b865769436a3e1f33",
+            "max": 100000,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_4c3c33ad6f484d30b091858c13ded0da",
+            "value": 100000
+          }
+        },
+        "f3c1148efd194daeae69b8dbcc24c9d5": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "DescriptionStyleModel",
           "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_176df16de19f4d32ac1b85c5c2082b40",
+              "IPY_MODEL_b1fe6058815a412d968e69beea11ecb5",
+              "IPY_MODEL_06299c873ad44899a078291779f6e8e4"
+            ],
+            "layout": "IPY_MODEL_ac6c0a7f4a574b2880c6e7a76c54f82b"
+          }
+        },
+        "f4a0ae32f65a45e4b2015e164c7963be": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
           "state": {
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
@@ -2970,9 +2964,8 @@
           }
         }
       }
-    },
-    "accelerator": "GPU"
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}

From 42641fff123a0a14b112e0ffcbd0d54353774798 Mon Sep 17 00:00:00 2001
From: Harshith K <kharshith@google.com>
Date: Mon, 14 Jul 2025 12:33:59 +0530
Subject: [PATCH 04/11] Modified dlrm.ipynb to add architecture Diagram and
 reference to paper

---
 examples/keras_rs/dlrm.py          | 52 +++++++++++++++++-------------
 examples/keras_rs/ipynb/DLRM.ipynb | 21 ++++--------
 2 files changed, 36 insertions(+), 37 deletions(-)

diff --git a/examples/keras_rs/dlrm.py b/examples/keras_rs/dlrm.py
index aa66cbd5c2..af357f99e8 100644
--- a/examples/keras_rs/dlrm.py
+++ b/examples/keras_rs/dlrm.py
@@ -9,30 +9,38 @@
 """
 ## Introduction
 
-This tutorial demonstrates how to use Deep Learning Recommendation Model (DLRM) to
-effectively learn relationship between movies and ratings using DotInteraction
-
-The Deep Learning Recommendation Model (DLRM) architecture is designed for more effective
-learning of explicit and bounded-degree features which performs well on both categrical and 
-continuous (sparse/dense) features. It comprises of three main components: input layers 
-(typically an embedding layer for each feature), a dot interaction layer for modeling explicit 
-feature interactions, and MLP based deep network for capturing implicit interactions.
-
-The dot interaction layer is the core of the DLRM, which computes the pairwise dot
-products between different feature embeddings.
-
-The deep network is a standard feedforward multilayer perceptron
-(MLP). These two networks are then combined to form the DLRM.
-
-Now that we know a little bit about DLRM, let's start writing some code. We will
-first train a DLRM on a real-world dataset, and demonstrate that the model has indeed
-learnt important feature crosses.
-
-Let's set the backend to JAX, and get our imports sorted.
+This tutorial demonstrates how to use the Deep Learning Recommendation Model (DLRM) to
+effectively learn the relationships between items and user preferences using a
+dot-product interaction mechanism. For more details, please refer to the
+[DLRM](https://arxiv.org/pdf/1906.00091) paper.
+
+DLRM is designed to excel at capturing explicit, bounded-degree feature interactions and
+is particularly effective at processing both categorical and continuous (sparse/dense)
+input features. The architecture consists of three main components: dedicated input
+layers to handle diverse features (typically embedding layers for categorical features),
+a dot-product interaction layer to explicitly model feature interactions, and a
+Multi-Layer Perceptron (MLP) to capture implicit feature relationships.
+
+The dot-product interaction layer lies at the heart of DLRM, efficiently computing
+pairwise interactions between different feature embeddings. This contrasts with models
+like Deep & Cross Network (DCN), which can treat elements within a feature vector as
+independent units, potentially leading to a higher-dimensional space and increased
+computational cost. The MLP is a standard feedforward network. The DLRM is formed by
+combining the interaction layer and MLP.
+
+The following image illustrates the DLRM architecture:
+
+![DLRM Architecture](https://raw.githubusercontent.com/kharshith-k/keras-io/refs/heads/keras-rs-examples/examples/keras_rs/img/dlrm/dlrm_architecture.gif)
+
+
+Now that we have a foundational understanding of DLRM's architecture and key
+characteristics, let's dive into the code. We will train a DLRM on a real-world dataset
+to demonstrate its capability to learn meaningful feature interactions. Let's begin by
+setting the backend to JAX and organizing our imports.
 """
 
 """shell
-!pip install  jax_tpu_embedding keras-rs
+!pip install keras-rs
 """
 
 import os
@@ -330,7 +338,7 @@ def call(self, inputs):
 
 The DotInteraction layer itself doesn't have a conventional "weight" matrix like a Dense
 layer. Instead, its function is to compute the dot product between the embedding vectors
-of features.
+of your features.
 
 To visualize the strength of these interactions, we can calculate a matrix representing
 the pairwise interaction strength between all feature embeddings. A common way to do this
diff --git a/examples/keras_rs/ipynb/DLRM.ipynb b/examples/keras_rs/ipynb/DLRM.ipynb
index b7b7dfacb1..1fac111368 100644
--- a/examples/keras_rs/ipynb/DLRM.ipynb
+++ b/examples/keras_rs/ipynb/DLRM.ipynb
@@ -8,27 +8,18 @@
       "source": [
         "## Introduction\n",
         "\n",
-        "This tutorial demonstrates how to use Deep Learning Recommendation Model (DLRM) to effectively\n",
-        "learn relationship between movies and ratings using DotInteraction. For more details, please refer to the [DLRM](https://arxiv.org/pdf/1906.00091) paper.\n",
+        "This tutorial demonstrates how to use the Deep Learning Recommendation Model (DLRM) to effectively learn the relationships between items and user preferences using a dot-product interaction mechanism. For more details, please refer to the [DLRM](https://arxiv.org/pdf/1906.00091) paper.\n",
         "\n",
-        "The Deep Learning Recommendation Model (DLRM) architecture is designed for more effective\n",
-        "learning of explicit and bounded-degree features which performs well on both categrical and \n",
-        "continuous (sparse/dense) features. It comprises of three main components: input layers \n",
-        "(typically an embedding layer for each feature), a dot interaction layer for modeling explicit \n",
-        "feature interactions, and MLP based deep network for capturing implicit interactions.\n",
+        "DLRM is designed to excel at capturing explicit, bounded-degree feature interactions and is particularly effective at processing both categorical and continuous (sparse/dense) input features. The architecture consists of three main components: dedicated input layers to handle diverse features (typically embedding layers for categorical features), a dot-product interaction layer to explicitly model feature interactions, and a Multi-Layer Perceptron (MLP) to capture implicit feature relationships.\n",
         "\n",
-        "The dot interaction layer is the core of the DLRM, which computes the pairwise dot products between different feature embeddings.\n",
+        "The dot-product interaction layer lies at the heart of DLRM, efficiently computing pairwise interactions between different feature embeddings. This contrasts with models like Deep & Cross Network (DCN), which can treat elements within a feature vector as independent units, potentially leading to a higher-dimensional space and increased computational cost. The MLP is a standard feedforward network. The DLRM is formed by combining the interaction layer and MLP.\n",
         "\n",
-        "The deep network is a standard feedforward multilayer perceptron\n",
-        "(MLP). These two networks are then combined to form the DLRM.\n",
+        "The following image illustrates the DLRM architecture:\n",
         "\n",
-        "![DLRM Architecture](img/dlrm/dlrm_architecture.gif)\n",
+        "![DLRM Architecture](https://raw.githubusercontent.com/kharshith-k/keras-io/refs/heads/keras-rs-examples/examples/keras_rs/img/dlrm/dlrm_architecture.gif)\n",
         "\n",
-        "Now that we know a little bit about DLRM, let's start writing some code. We will\n",
-        "first train a DLRM on a real-world dataset, and demonstrate that the model has indeed\n",
-        "learnt important feature crosses.\n",
         "\n",
-        "Let's set the backend to JAX, and get our imports sorted."
+        "Now that we have a foundational understanding of DLRM's architecture and key characteristics, let's dive into the code. We will train a DLRM on a real-world dataset to demonstrate its capability to learn meaningful feature interactions. Let's begin by setting the backend to JAX and organizing our imports."
       ]
     },
     {

From 1f37ad34a453d802905b2da2cb4c953a4715101f Mon Sep 17 00:00:00 2001
From: kharshith-k <kharshith@google.com>
Date: Mon, 14 Jul 2025 12:40:31 +0530
Subject: [PATCH 05/11] Added dlrm.ipynb

---
 examples/keras_rs/ipynb/dlrm.ipynb | 2962 ++++++++++++++++++++++++++++
 1 file changed, 2962 insertions(+)
 create mode 100644 examples/keras_rs/ipynb/dlrm.ipynb

diff --git a/examples/keras_rs/ipynb/dlrm.ipynb b/examples/keras_rs/ipynb/dlrm.ipynb
new file mode 100644
index 0000000000..1fac111368
--- /dev/null
+++ b/examples/keras_rs/ipynb/dlrm.ipynb
@@ -0,0 +1,2962 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33c74727"
+      },
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This tutorial demonstrates how to use the Deep Learning Recommendation Model (DLRM) to effectively learn the relationships between items and user preferences using a dot-product interaction mechanism. For more details, please refer to the [DLRM](https://arxiv.org/pdf/1906.00091) paper.\n",
+        "\n",
+        "DLRM is designed to excel at capturing explicit, bounded-degree feature interactions and is particularly effective at processing both categorical and continuous (sparse/dense) input features. The architecture consists of three main components: dedicated input layers to handle diverse features (typically embedding layers for categorical features), a dot-product interaction layer to explicitly model feature interactions, and a Multi-Layer Perceptron (MLP) to capture implicit feature relationships.\n",
+        "\n",
+        "The dot-product interaction layer lies at the heart of DLRM, efficiently computing pairwise interactions between different feature embeddings. This contrasts with models like Deep & Cross Network (DCN), which can treat elements within a feature vector as independent units, potentially leading to a higher-dimensional space and increased computational cost. The MLP is a standard feedforward network. The DLRM is formed by combining the interaction layer and MLP.\n",
+        "\n",
+        "The following image illustrates the DLRM architecture:\n",
+        "\n",
+        "![DLRM Architecture](https://raw.githubusercontent.com/kharshith-k/keras-io/refs/heads/keras-rs-examples/examples/keras_rs/img/dlrm/dlrm_architecture.gif)\n",
+        "\n",
+        "\n",
+        "Now that we have a foundational understanding of DLRM's architecture and key characteristics, let's dive into the code. We will train a DLRM on a real-world dataset to demonstrate its capability to learn meaningful feature interactions. Let's begin by setting the backend to JAX and organizing our imports."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "blj4_0Wg62kR",
+        "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: keras-rs in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (0.2.1)\n",
+            "Requirement already satisfied: keras in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (3.10.0)\n",
+            "Requirement already satisfied: ml-dtypes in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (0.5.1)\n",
+            "Requirement already satisfied: absl-py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.3.1)\n",
+            "Requirement already satisfied: numpy in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.1.3)\n",
+            "Requirement already satisfied: rich in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (14.0.0)\n",
+            "Requirement already satisfied: namex in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.1.0)\n",
+            "Requirement already satisfied: h5py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (3.14.0)\n",
+            "Requirement already satisfied: optree in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.16.0)\n",
+            "Requirement already satisfied: packaging in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (25.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from optree->keras->keras-rs) (4.14.1)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (2.19.2)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->keras->keras-rs) (0.1.2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install keras-rs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "QeFXrN1962kT"
+      },
+      "outputs": [
+        {
+          "ename": "ModuleNotFoundError",
+          "evalue": "No module named 'jax'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m      3\u001b[39m os.environ[\u001b[33m\"\u001b[39m\u001b[33mKERAS_BACKEND\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# `\"tensorflow\"`/`\"torch\"`\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _tf_keras \u001b[38;5;28;01mas\u001b[39;00m _tf_keras\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_tf_keras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m callbacks \u001b[38;5;28;01mas\u001b[39;00m callbacks\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/activations/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;28;01mas\u001b[39;00m deserialize\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get \u001b[38;5;28;01mas\u001b[39;00m get\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m serialize \u001b[38;5;28;01mas\u001b[39;00m serialize\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtypes\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m celu\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m exponential\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/activations.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi_export\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/__init__.py:41\u001b[39m\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtensorflow\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     42\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mtorch\u001b[39m\u001b[33m\"\u001b[39m:\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m distribution_lib\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m image\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/core.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexperimental\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax_sparse\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjnp\u001b[39;00m\n",
+            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'jax'"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # `\"tensorflow\"`/`\"torch\"`\n",
+        "\n",
+        "import keras\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "import tensorflow_datasets as tfds\n",
+        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+        "\n",
+        "import keras_rs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gHdT0D1762kT"
+      },
+      "source": [
+        "Let's also define variables which will be reused throughout the example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GRJOAiMv62kT"
+      },
+      "outputs": [],
+      "source": [
+        "MOVIELENS_CONFIG = {\n",
+        "    # features\n",
+        "    \"int_features\": [\n",
+        "        \"movie_id\",\n",
+        "        \"user_id\",\n",
+        "        \"user_gender\",\n",
+        "        \"bucketized_user_age\",\n",
+        "    ],\n",
+        "    \"str_features\": [\n",
+        "        \"user_zip_code\",\n",
+        "        \"user_occupation_text\",\n",
+        "    ],\n",
+        "    # model\n",
+        "    \"embedding_dim\": 8,\n",
+        "    \"deep_net_num_units\": [192, 192, 192],\n",
+        "    # training\n",
+        "    \"learning_rate\": 1e-2,\n",
+        "    \"num_epochs\": 25,\n",
+        "    \"batch_size\": 8192,\n",
+        "}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_6J6XFNC62kT"
+      },
+      "source": [
+        "Here, we define a helper function for visualising weights of the cross layer in\n",
+        "order to better understand its functioning. Also, we define a function for\n",
+        "compiling, training and evaluating a given model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jyojSTKw62kT"
+      },
+      "outputs": [],
+      "source": [
+        "def plot_training_metrics(history):\n",
+        "    \"\"\"Graphs all metrics tracked in the history object.\"\"\"\n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "\n",
+        "    for metric_name, metric_values in history.history.items():\n",
+        "        plt.plot(metric_values, label=metric_name.replace('_', ' ').title())\n",
+        "\n",
+        "    plt.title('Metrics over Epochs')\n",
+        "    plt.xlabel('Epoch')\n",
+        "    plt.ylabel('Metric Value')\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "\n",
+        "def visualize_layer(matrix, features, cmap=plt.cm.Blues):\n",
+        "    plt.figure(figsize=(9, 9))\n",
+        "\n",
+        "    im = plt.matshow(matrix, cmap=cmap)\n",
+        "\n",
+        "    ax = plt.gca()\n",
+        "    divider = make_axes_locatable(plt.gca())\n",
+        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "    plt.colorbar(im, cax=cax)\n",
+        "    cax.tick_params(labelsize=10)\n",
+        "\n",
+        "    # Set tick locations explicitly before setting labels\n",
+        "    ax.set_xticks(np.arange(len(features)))\n",
+        "    ax.set_yticks(np.arange(len(features)))\n",
+        "\n",
+        "    ax.set_xticklabels(features, rotation=45, fontsize=5)\n",
+        "    ax.set_yticklabels(features, fontsize=5)\n",
+        "\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "def train_and_evaluate(\n",
+        "    learning_rate,\n",
+        "    epochs,\n",
+        "    train_data,\n",
+        "    test_data,\n",
+        "    model,\n",
+        "    plot_metrics=False,\n",
+        "):\n",
+        "    optimizer = keras.optimizers.AdamW(learning_rate=learning_rate)\n",
+        "    loss = keras.losses.MeanSquaredError()\n",
+        "    rmse = keras.metrics.RootMeanSquaredError()\n",
+        "\n",
+        "    model.compile(\n",
+        "        optimizer=optimizer,\n",
+        "        loss=loss,\n",
+        "        metrics=[rmse],\n",
+        "    )\n",
+        "\n",
+        "    history = model.fit(\n",
+        "        train_data,\n",
+        "        epochs=epochs,\n",
+        "        verbose=1,\n",
+        "    )\n",
+        "    if plot_metrics:\n",
+        "      plot_training_metrics(history)\n",
+        "\n",
+        "    results = model.evaluate(test_data, return_dict=True, verbose=1)\n",
+        "    rmse_value = results[\"root_mean_squared_error\"]\n",
+        "\n",
+        "    return rmse_value, model.count_params()\n",
+        "\n",
+        "\n",
+        "def print_stats(rmse_list, num_params, model_name):\n",
+        "    # Report metrics.\n",
+        "    num_trials = len(rmse_list)\n",
+        "    avg_rmse = np.mean(rmse_list)\n",
+        "    std_rmse = np.std(rmse_list)\n",
+        "\n",
+        "    if num_trials == 1:\n",
+        "        print(f\"{model_name}: RMSE = {avg_rmse}; #params = {num_params}\")\n",
+        "    else:\n",
+        "        print(f\"{model_name}: RMSE = {avg_rmse} ± {std_rmse}; #params = {num_params}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tVHJBIJ_62kV"
+      },
+      "source": [
+        "## Real-world example\n",
+        "\n",
+        "Let's use the MovieLens 100K dataset. This dataset is used to train models to\n",
+        "predict users' movie ratings, based on user-related features and movie-related\n",
+        "features.\n",
+        "\n",
+        "### Preparing the dataset\n",
+        "\n",
+        "The dataset processing steps here are similar to what's given in the\n",
+        "[basic ranking](/keras_rs/examples/basic_ranking/)\n",
+        "tutorial. Let's load the dataset, and keep only the useful columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187,
+          "referenced_widgets": [
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+            "5643b587cb634078bcf7b3cc637c23e0",
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+            "4643620958e34bf685682c3f44780370",
+            "d86b9434231b4cd28ce1611e778454c2",
+            "d5804d8334c54434a447f19188ee751f",
+            "a1fefc93f4ca4fc9866774e28308d62d",
+            "1115049423874cd78f3b85184f8d7e15",
+            "7986925eb5624324aee93da08a630099",
+            "2e706f8ef4d34b39b092199c17262dd7",
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+            "25df22d29aab44578894f0aa0ce53aa4",
+            "6898fd5bc3634fe681a96bd01b629b53",
+            "ec2a121d9157485d9002795e0c86d471",
+            "6a0a7673658a4d77921bd05493ad50b6",
+            "f0b8e85a1e854efa8a43b9ada4109915",
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+            "71f75584dc4041a0adc23a87dbedc2c2",
+            "12605d31b53048008fabf7e93ebab4c5",
+            "f4a0ae32f65a45e4b2015e164c7963be",
+            "cf2d1c56ac624e7b865769436a3e1f33",
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+            "78cdea4a88414d109466b4b1ad60ad18",
+            "11ac95d724414a02aec0f5424b271b41"
+          ]
+        },
+        "id": "kipGDLuZ62kW",
+        "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:absl:Variant folder /root/tensorflow_datasets/movielens/100k-ratings/0.1.1 has no dataset_info.json\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "58dfd53b556843dabaecd6cf2d6935ef",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Completed...: 0 url [00:00, ? url/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f3c1148efd194daeae69b8dbcc24c9d5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "dc7d6718b55b499ebde1bd8c3b77c108",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Extraction completed...: 0 file [00:00, ? file/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "024cbbb854c943319b9be0d743e09f84",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6c26c23d5e9544e8b114c816ad667320",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train examples...: 0 examples [00:00, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ec2a121d9157485d9002795e0c86d471",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1. Subsequent calls will reuse this data.\n"
+          ]
+        }
+      ],
+      "source": [
+        "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
+        "ratings_ds = ratings_ds.map(\n",
+        "    lambda x: (\n",
+        "        {\n",
+        "            \"movie_id\": int(x[\"movie_id\"]),\n",
+        "            \"user_id\": int(x[\"user_id\"]),\n",
+        "            \"user_gender\": int(x[\"user_gender\"]),\n",
+        "            \"user_zip_code\": x[\"user_zip_code\"],\n",
+        "            \"user_occupation_text\": x[\"user_occupation_text\"],\n",
+        "            \"bucketized_user_age\": int(x[\"bucketized_user_age\"]),\n",
+        "        },\n",
+        "        x[\"user_rating\"],  # label\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3DrNFuZi62kW"
+      },
+      "source": [
+        "For every feature, let's get the list of unique values, i.e., vocabulary, so\n",
+        "that we can use that for the embedding layer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pczAh0MV62kW"
+      },
+      "outputs": [],
+      "source": [
+        "vocabularies = {}\n",
+        "for feature_name in MOVIELENS_CONFIG[\"int_features\"] + MOVIELENS_CONFIG[\"str_features\"]:\n",
+        "    vocabulary = ratings_ds.batch(10_000).map(lambda x, y: x[feature_name])\n",
+        "    vocabularies[feature_name] = np.unique(np.concatenate(list(vocabulary)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1PLhYqF062kW"
+      },
+      "source": [
+        "One thing we need to do is to use `keras.layers.StringLookup` and\n",
+        "`keras.layers.IntegerLookup` to convert all features into indices, which can\n",
+        "then be fed into embedding layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pLMvMAIF62kW"
+      },
+      "outputs": [],
+      "source": [
+        "lookup_layers = {}\n",
+        "lookup_layers.update(\n",
+        "    {\n",
+        "        feature: keras.layers.IntegerLookup(vocabulary=vocabularies[feature])\n",
+        "        for feature in MOVIELENS_CONFIG[\"int_features\"]\n",
+        "    }\n",
+        ")\n",
+        "lookup_layers.update(\n",
+        "    {\n",
+        "        feature: keras.layers.StringLookup(vocabulary=vocabularies[feature])\n",
+        "        for feature in MOVIELENS_CONFIG[\"str_features\"]\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "ratings_ds = ratings_ds.map(\n",
+        "    lambda x, y: (\n",
+        "        {\n",
+        "            feature_name: lookup_layers[feature_name](x[feature_name])\n",
+        "            for feature_name in vocabularies\n",
+        "        },\n",
+        "        y,\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XBKk-mX962kW"
+      },
+      "source": [
+        "Let's split our data into train and test sets. We also use `cache()` and\n",
+        "`prefetch()` for better performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ha3v4rHN62kW"
+      },
+      "outputs": [],
+      "source": [
+        "ratings_ds = ratings_ds.shuffle(100_000)\n",
+        "\n",
+        "train_ds = (\n",
+        "    ratings_ds.take(80_000)\n",
+        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
+        "    .cache()\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")\n",
+        "test_ds = (\n",
+        "    ratings_ds.skip(80_000)\n",
+        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
+        "    .take(20_000)\n",
+        "    .cache()\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RXuXvsl062kW"
+      },
+      "source": [
+        "### Building the model\n",
+        "\n",
+        "The model will have embedding layers, followed by DotInteraction and feedforward\n",
+        "layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ww3ZJ6R062kW"
+      },
+      "outputs": [],
+      "source": [
+        "class DLRM(keras.Model):\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        dense_num_units_lst,\n",
+        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        "        **kwargs,\n",
+        "    ):\n",
+        "        super().__init__(**kwargs)\n",
+        "\n",
+        "        # Layers.\n",
+        "\n",
+        "        self.embedding_layers = []\n",
+        "        for feature_name, vocabulary in vocabularies.items():\n",
+        "            self.embedding_layers.append(\n",
+        "                keras.layers.Embedding(\n",
+        "                    input_dim=len(vocabulary) + 1,\n",
+        "                    output_dim=embedding_dim,\n",
+        "                )\n",
+        "            )\n",
+        "\n",
+        "        self.dot_layer = keras_rs.layers.DotInteraction()\n",
+        "\n",
+        "        self.dense_layers = []\n",
+        "        for num_units in dense_num_units_lst:\n",
+        "            self.dense_layers.append(keras.layers.Dense(num_units, activation=\"relu\"))\n",
+        "\n",
+        "        self.output_layer = keras.layers.Dense(1)\n",
+        "\n",
+        "        # Attributes.\n",
+        "        self.dense_num_units_lst = dense_num_units_lst\n",
+        "        self.embedding_dim = embedding_dim\n",
+        "\n",
+        "    def call(self, inputs):\n",
+        "        embeddings = []\n",
+        "        for feature_name, embedding_layer in zip(vocabularies, self.embedding_layers):\n",
+        "            embeddings.append(embedding_layer(inputs[feature_name]))\n",
+        "\n",
+        "        # Pass the list of embeddings to the DotInteraction layer\n",
+        "        x = self.dot_layer(embeddings)\n",
+        "\n",
+        "        for dense_layer in self.dense_layers:\n",
+        "            x = dense_layer(x)\n",
+        "\n",
+        "        x = self.output_layer(x)\n",
+        "\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "OAoTyyzi_Nau",
+        "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - loss: 8.1493 - root_mean_squared_error: 2.8203\n",
+            "Epoch 2/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 2.8909 - root_mean_squared_error: 1.6905\n",
+            "Epoch 3/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5062 - root_mean_squared_error: 1.2266\n",
+            "Epoch 4/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2285 - root_mean_squared_error: 1.1076\n",
+            "Epoch 5/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.9398 - root_mean_squared_error: 0.9694 \n",
+            "Epoch 6/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.8562 - root_mean_squared_error: 0.9253\n",
+            "Epoch 7/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.7850 - root_mean_squared_error: 0.8860\n",
+            "Epoch 8/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7260 - root_mean_squared_error: 0.8520 \n",
+            "Epoch 9/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6800 - root_mean_squared_error: 0.8246 \n",
+            "Epoch 10/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.6444 - root_mean_squared_error: 0.8028\n",
+            "Epoch 11/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6183 - root_mean_squared_error: 0.7863 \n",
+            "Epoch 12/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5971 - root_mean_squared_error: 0.7727\n",
+            "Epoch 13/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5808 - root_mean_squared_error: 0.7621\n",
+            "Epoch 14/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5676 - root_mean_squared_error: 0.7534 \n",
+            "Epoch 15/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5566 - root_mean_squared_error: 0.7461\n",
+            "Epoch 16/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5474 - root_mean_squared_error: 0.7399\n",
+            "Epoch 17/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5399 - root_mean_squared_error: 0.7347 \n",
+            "Epoch 18/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5333 - root_mean_squared_error: 0.7303\n",
+            "Epoch 19/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5277 - root_mean_squared_error: 0.7264\n",
+            "Epoch 20/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5233 - root_mean_squared_error: 0.7234 \n",
+            "Epoch 21/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5191 - root_mean_squared_error: 0.7205 \n",
+            "Epoch 22/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5156 - root_mean_squared_error: 0.7180\n",
+            "Epoch 23/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5124 - root_mean_squared_error: 0.7158\n",
+            "Epoch 24/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5097 - root_mean_squared_error: 0.7139\n",
+            "Epoch 25/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5069 - root_mean_squared_error: 0.7120 \n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 513ms/step - loss: 0.6062 - root_mean_squared_error: 0.7786\n",
+            "Dot Network: RMSE = 0.7789321541786194; #params = 105025\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "dot_network = DLRM(\n",
+        "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
+        "    embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        ")\n",
+        "rmse, dot_network_num_params = train_and_evaluate(\n",
+        "    learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
+        "    epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
+        "    train_data=train_ds,\n",
+        "    test_data=test_ds,\n",
+        "    model= dot_network,\n",
+        "    plot_metrics=True\n",
+        ")\n",
+        "print_stats(\n",
+        "    rmse_list=[rmse],\n",
+        "    num_params=dot_network_num_params,\n",
+        "    model_name=\"Dot Network\",\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E68GrcDN62kW"
+      },
+      "source": [
+        "### Visualizing feature interactions\n",
+        "\n",
+        "The DotInteraction layer itself doesn't have a conventional \"weight\" matrix like a Dense layer. Instead, its function is to compute the dot product between the embedding vectors of your features.\n",
+        "\n",
+        "To visualize the strength of these interactions, we can calculate a matrix representing the pairwise interaction strength between all feature embeddings. A common way to do this is to take the dot product of the embedding matrices for each pair of features and then aggregate the result into a single value (like the mean of the absolute values) that represents the overall interaction strength."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R8WPVVn-Jq81"
+      },
+      "outputs": [],
+      "source": [
+        "def get_dot_interaction_matrix(model, feature_names):\n",
+        "    # Extract the trained embedding weights from the model\n",
+        "    embedding_weights = [layer.get_weights()[0] for layer in model.embedding_layers]\n",
+        "\n",
+        "    num_features = len(feature_names)\n",
+        "    interaction_matrix = np.zeros((num_features, num_features))\n",
+        "\n",
+        "    # Iterate through each pair of features to calculate their interaction strength\n",
+        "    for i in range(num_features):\n",
+        "        for j in range(num_features):\n",
+        "            # Calculate the dot product between the two embedding matrices\n",
+        "            interaction = np.dot(embedding_weights[i], embedding_weights[j].T)\n",
+        "            # Take the mean of the absolute values as a measure of interaction strength\n",
+        "            interaction_strength = np.mean(np.abs(interaction))\n",
+        "            interaction_matrix[i, j] = interaction_strength\n",
+        "\n",
+        "    return interaction_matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 512
+        },
+        "id": "NH-VtGrMJtAt",
+        "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Visualizing the feature interaction strengths:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 900x900 with 0 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 480x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Get the list of feature names in the correct order\n",
+        "feature_names = list(vocabularies.keys())\n",
+        "\n",
+        "# Calculate the interaction matrix\n",
+        "interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)\n",
+        "\n",
+        "# Visualize the matrix as a heatmap\n",
+        "print(\"\\nVisualizing the feature interaction strengths:\")\n",
+        "visualize_layer(interaction_matrix, feature_names)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IbpjtvnQ62kW"
+      },
+      "outputs": [],
+      "source": [
+        "dlrm_rmse_list = []\n",
+        "\n",
+        "for _ in range(20):\n",
+        "\n",
+        "    dot_network = DLRM(\n",
+        "        dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
+        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        "    )\n",
+        "    rmse, dot_network_num_params = train_and_evaluate(\n",
+        "        learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
+        "        epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
+        "        train_data=train_ds,\n",
+        "        test_data=test_ds,\n",
+        "        model= dot_network,\n",
+        "    )\n",
+        "    dlrm_rmse_list.append(rmse)\n",
+        "\n",
+        "print_stats(\n",
+        "    rmse_list=dlrm_rmse_list,\n",
+        "    num_params=dot_network_num_params,\n",
+        "    model_name=\"Dot Network\",\n",
+        ")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8mskfVqa-uGx"
+      },
+      "outputs": [],
+      "source": [
+        "def plot_rmse(rmse_list, model_name):\n",
+        "  plt.figure()\n",
+        "  plt.plot(rmse_list)\n",
+        "  plt.title(f'RMSE over trials for {model_name}')\n",
+        "  plt.xlabel('Trial')\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.show()\n",
+        "\n",
+        "plot_rmse(dlrm_rmse_list, \"Dot Network\")"
+      ]
+    }
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From 01b2257f6236aa469c39f6c17eb053f9cdf93635 Mon Sep 17 00:00:00 2001
From: kharshith-k <kharshith@google.com>
Date: Mon, 14 Jul 2025 12:45:28 +0530
Subject: [PATCH 06/11] Added dlrm.py

---
 examples/keras_rs/ipynb/dlrm.ipynb | 260 +----------------------------
 1 file changed, 7 insertions(+), 253 deletions(-)

diff --git a/examples/keras_rs/ipynb/dlrm.ipynb b/examples/keras_rs/ipynb/dlrm.ipynb
index 1fac111368..c0b410ae69 100644
--- a/examples/keras_rs/ipynb/dlrm.ipynb
+++ b/examples/keras_rs/ipynb/dlrm.ipynb
@@ -24,7 +24,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -32,61 +32,18 @@
         "id": "blj4_0Wg62kR",
         "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: keras-rs in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (0.2.1)\n",
-            "Requirement already satisfied: keras in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (3.10.0)\n",
-            "Requirement already satisfied: ml-dtypes in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (0.5.1)\n",
-            "Requirement already satisfied: absl-py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.3.1)\n",
-            "Requirement already satisfied: numpy in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.1.3)\n",
-            "Requirement already satisfied: rich in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (14.0.0)\n",
-            "Requirement already satisfied: namex in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.1.0)\n",
-            "Requirement already satisfied: h5py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (3.14.0)\n",
-            "Requirement already satisfied: optree in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.16.0)\n",
-            "Requirement already satisfied: packaging in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (25.0)\n",
-            "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from optree->keras->keras-rs) (4.14.1)\n",
-            "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (3.0.0)\n",
-            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (2.19.2)\n",
-            "Requirement already satisfied: mdurl~=0.1 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->keras->keras-rs) (0.1.2)\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "!pip install keras-rs"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
         "id": "QeFXrN1962kT"
       },
-      "outputs": [
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-          "ename": "ModuleNotFoundError",
-          "evalue": "No module named 'jax'",
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-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_tf_keras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m callbacks \u001b[38;5;28;01mas\u001b[39;00m callbacks\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/activations/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;28;01mas\u001b[39;00m deserialize\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get \u001b[38;5;28;01mas\u001b[39;00m get\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m serialize \u001b[38;5;28;01mas\u001b[39;00m serialize\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtypes\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m celu\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m exponential\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/activations.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi_export\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/__init__.py:41\u001b[39m\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtensorflow\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     42\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mtorch\u001b[39m\u001b[33m\"\u001b[39m:\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m distribution_lib\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m image\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/core.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexperimental\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax_sparse\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjnp\u001b[39;00m\n",
-            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'jax'"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "import os\n",
         "\n",
@@ -336,113 +293,7 @@
         "id": "kipGDLuZ62kW",
         "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "WARNING:absl:Variant folder /root/tensorflow_datasets/movielens/100k-ratings/0.1.1 has no dataset_info.json\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "58dfd53b556843dabaecd6cf2d6935ef",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Dl Completed...: 0 url [00:00, ? url/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "f3c1148efd194daeae69b8dbcc24c9d5",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "dc7d6718b55b499ebde1bd8c3b77c108",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Extraction completed...: 0 file [00:00, ? file/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "024cbbb854c943319b9be0d743e09f84",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6c26c23d5e9544e8b114c816ad667320",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Generating train examples...: 0 examples [00:00, ? examples/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ec2a121d9157485d9002795e0c86d471",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Dataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1. Subsequent calls will reuse this data.\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
         "ratings_ds = ratings_ds.map(\n",
@@ -642,76 +493,7 @@
         "id": "OAoTyyzi_Nau",
         "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - loss: 8.1493 - root_mean_squared_error: 2.8203\n",
-            "Epoch 2/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 2.8909 - root_mean_squared_error: 1.6905\n",
-            "Epoch 3/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5062 - root_mean_squared_error: 1.2266\n",
-            "Epoch 4/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2285 - root_mean_squared_error: 1.1076\n",
-            "Epoch 5/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.9398 - root_mean_squared_error: 0.9694 \n",
-            "Epoch 6/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.8562 - root_mean_squared_error: 0.9253\n",
-            "Epoch 7/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.7850 - root_mean_squared_error: 0.8860\n",
-            "Epoch 8/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7260 - root_mean_squared_error: 0.8520 \n",
-            "Epoch 9/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6800 - root_mean_squared_error: 0.8246 \n",
-            "Epoch 10/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.6444 - root_mean_squared_error: 0.8028\n",
-            "Epoch 11/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6183 - root_mean_squared_error: 0.7863 \n",
-            "Epoch 12/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5971 - root_mean_squared_error: 0.7727\n",
-            "Epoch 13/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5808 - root_mean_squared_error: 0.7621\n",
-            "Epoch 14/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5676 - root_mean_squared_error: 0.7534 \n",
-            "Epoch 15/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5566 - root_mean_squared_error: 0.7461\n",
-            "Epoch 16/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5474 - root_mean_squared_error: 0.7399\n",
-            "Epoch 17/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5399 - root_mean_squared_error: 0.7347 \n",
-            "Epoch 18/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5333 - root_mean_squared_error: 0.7303\n",
-            "Epoch 19/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5277 - root_mean_squared_error: 0.7264\n",
-            "Epoch 20/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5233 - root_mean_squared_error: 0.7234 \n",
-            "Epoch 21/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5191 - root_mean_squared_error: 0.7205 \n",
-            "Epoch 22/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5156 - root_mean_squared_error: 0.7180\n",
-            "Epoch 23/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5124 - root_mean_squared_error: 0.7158\n",
-            "Epoch 24/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5097 - root_mean_squared_error: 0.7139\n",
-            "Epoch 25/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5069 - root_mean_squared_error: 0.7120 \n",
-            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 513ms/step - loss: 0.6062 - root_mean_squared_error: 0.7786\n",
-            "Dot Network: RMSE = 0.7789321541786194; #params = 105025\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1200x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "dot_network = DLRM(\n",
         "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
@@ -783,35 +565,7 @@
         "id": "NH-VtGrMJtAt",
         "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Visualizing the feature interaction strengths:\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<Figure size 900x900 with 0 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 480x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Get the list of feature names in the correct order\n",
         "feature_names = list(vocabularies.keys())\n",

From d02d683b95e9b77a653020a9859bda80f646173c Mon Sep 17 00:00:00 2001
From: Harshith K <kharshith@google.com>
Date: Mon, 11 Aug 2025 21:49:23 +0530
Subject: [PATCH 07/11] local dlrm.py

---
 examples/keras_rs/ipynb/dlrm.ipynb | 260 ++++++++++++++++++++++++++++-
 1 file changed, 253 insertions(+), 7 deletions(-)

diff --git a/examples/keras_rs/ipynb/dlrm.ipynb b/examples/keras_rs/ipynb/dlrm.ipynb
index c0b410ae69..1fac111368 100644
--- a/examples/keras_rs/ipynb/dlrm.ipynb
+++ b/examples/keras_rs/ipynb/dlrm.ipynb
@@ -24,7 +24,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -32,18 +32,61 @@
         "id": "blj4_0Wg62kR",
         "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: keras-rs in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (0.2.1)\n",
+            "Requirement already satisfied: keras in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (3.10.0)\n",
+            "Requirement already satisfied: ml-dtypes in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (0.5.1)\n",
+            "Requirement already satisfied: absl-py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.3.1)\n",
+            "Requirement already satisfied: numpy in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.1.3)\n",
+            "Requirement already satisfied: rich in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (14.0.0)\n",
+            "Requirement already satisfied: namex in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.1.0)\n",
+            "Requirement already satisfied: h5py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (3.14.0)\n",
+            "Requirement already satisfied: optree in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.16.0)\n",
+            "Requirement already satisfied: packaging in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (25.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from optree->keras->keras-rs) (4.14.1)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (2.19.2)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->keras->keras-rs) (0.1.2)\n"
+          ]
+        }
+      ],
       "source": [
         "!pip install keras-rs"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
         "id": "QeFXrN1962kT"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "ename": "ModuleNotFoundError",
+          "evalue": "No module named 'jax'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m      3\u001b[39m os.environ[\u001b[33m\"\u001b[39m\u001b[33mKERAS_BACKEND\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# `\"tensorflow\"`/`\"torch\"`\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _tf_keras \u001b[38;5;28;01mas\u001b[39;00m _tf_keras\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_tf_keras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m callbacks \u001b[38;5;28;01mas\u001b[39;00m callbacks\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/activations/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;28;01mas\u001b[39;00m deserialize\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get \u001b[38;5;28;01mas\u001b[39;00m get\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m serialize \u001b[38;5;28;01mas\u001b[39;00m serialize\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtypes\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m celu\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m exponential\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/activations.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi_export\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/__init__.py:41\u001b[39m\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtensorflow\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     42\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mtorch\u001b[39m\u001b[33m\"\u001b[39m:\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m distribution_lib\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m image\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/core.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexperimental\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax_sparse\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjnp\u001b[39;00m\n",
+            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'jax'"
+          ]
+        }
+      ],
       "source": [
         "import os\n",
         "\n",
@@ -293,7 +336,113 @@
         "id": "kipGDLuZ62kW",
         "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:absl:Variant folder /root/tensorflow_datasets/movielens/100k-ratings/0.1.1 has no dataset_info.json\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "58dfd53b556843dabaecd6cf2d6935ef",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Completed...: 0 url [00:00, ? url/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f3c1148efd194daeae69b8dbcc24c9d5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "dc7d6718b55b499ebde1bd8c3b77c108",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Extraction completed...: 0 file [00:00, ? file/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "024cbbb854c943319b9be0d743e09f84",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6c26c23d5e9544e8b114c816ad667320",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train examples...: 0 examples [00:00, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ec2a121d9157485d9002795e0c86d471",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1. Subsequent calls will reuse this data.\n"
+          ]
+        }
+      ],
       "source": [
         "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
         "ratings_ds = ratings_ds.map(\n",
@@ -493,7 +642,76 @@
         "id": "OAoTyyzi_Nau",
         "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - loss: 8.1493 - root_mean_squared_error: 2.8203\n",
+            "Epoch 2/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 2.8909 - root_mean_squared_error: 1.6905\n",
+            "Epoch 3/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5062 - root_mean_squared_error: 1.2266\n",
+            "Epoch 4/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2285 - root_mean_squared_error: 1.1076\n",
+            "Epoch 5/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.9398 - root_mean_squared_error: 0.9694 \n",
+            "Epoch 6/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.8562 - root_mean_squared_error: 0.9253\n",
+            "Epoch 7/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.7850 - root_mean_squared_error: 0.8860\n",
+            "Epoch 8/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7260 - root_mean_squared_error: 0.8520 \n",
+            "Epoch 9/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6800 - root_mean_squared_error: 0.8246 \n",
+            "Epoch 10/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.6444 - root_mean_squared_error: 0.8028\n",
+            "Epoch 11/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6183 - root_mean_squared_error: 0.7863 \n",
+            "Epoch 12/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5971 - root_mean_squared_error: 0.7727\n",
+            "Epoch 13/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5808 - root_mean_squared_error: 0.7621\n",
+            "Epoch 14/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5676 - root_mean_squared_error: 0.7534 \n",
+            "Epoch 15/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5566 - root_mean_squared_error: 0.7461\n",
+            "Epoch 16/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5474 - root_mean_squared_error: 0.7399\n",
+            "Epoch 17/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5399 - root_mean_squared_error: 0.7347 \n",
+            "Epoch 18/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5333 - root_mean_squared_error: 0.7303\n",
+            "Epoch 19/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5277 - root_mean_squared_error: 0.7264\n",
+            "Epoch 20/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5233 - root_mean_squared_error: 0.7234 \n",
+            "Epoch 21/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5191 - root_mean_squared_error: 0.7205 \n",
+            "Epoch 22/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5156 - root_mean_squared_error: 0.7180\n",
+            "Epoch 23/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5124 - root_mean_squared_error: 0.7158\n",
+            "Epoch 24/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5097 - root_mean_squared_error: 0.7139\n",
+            "Epoch 25/25\n",
+            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5069 - root_mean_squared_error: 0.7120 \n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 513ms/step - loss: 0.6062 - root_mean_squared_error: 0.7786\n",
+            "Dot Network: RMSE = 0.7789321541786194; #params = 105025\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "dot_network = DLRM(\n",
         "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
@@ -565,7 +783,35 @@
         "id": "NH-VtGrMJtAt",
         "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Visualizing the feature interaction strengths:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 900x900 with 0 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 480x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "# Get the list of feature names in the correct order\n",
         "feature_names = list(vocabularies.keys())\n",

From 208c721b3c9912c57fa1e9e7e7d40877f0c494b6 Mon Sep 17 00:00:00 2001
From: Harshith K <kharshith@google.com>
Date: Mon, 11 Aug 2025 21:47:12 +0530
Subject: [PATCH 08/11] Removed .DS_Store files

---
 .DS_Store                          | Bin 6148 -> 0 bytes
 .gitignore                         |   1 +
 examples/.DS_Store                 | Bin 6148 -> 0 bytes
 examples/keras_rs/.DS_Store        | Bin 6148 -> 0 bytes
 examples/keras_rs/img/.DS_Store    | Bin 6148 -> 0 bytes
 examples/keras_rs/ipynb/DLRM.ipynb | 260 +----------------------------
 6 files changed, 8 insertions(+), 253 deletions(-)
 delete mode 100644 .DS_Store
 delete mode 100644 examples/.DS_Store
 delete mode 100644 examples/keras_rs/.DS_Store
 delete mode 100644 examples/keras_rs/img/.DS_Store

diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 94e8e06d0b3c0eea52a799b60b44125cd5fb1383..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 6148
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diff --git a/.gitignore b/.gitignore
index 7f756fb5a0..d646eb5568 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 tmp/*
 sources/*
 site/*
+*.DS_Store
 *.pyc
 *.swp
 templates/examples/audio/*
diff --git a/examples/.DS_Store b/examples/.DS_Store
deleted file mode 100644
index c227b428b6b89935df04fdb4f67b3d8da364afec..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/examples/keras_rs/.DS_Store b/examples/keras_rs/.DS_Store
deleted file mode 100644
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GIT binary patch
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deleted file mode 100644
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GIT binary patch
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diff --git a/examples/keras_rs/ipynb/DLRM.ipynb b/examples/keras_rs/ipynb/DLRM.ipynb
index 1fac111368..c0b410ae69 100644
--- a/examples/keras_rs/ipynb/DLRM.ipynb
+++ b/examples/keras_rs/ipynb/DLRM.ipynb
@@ -24,7 +24,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -32,61 +32,18 @@
         "id": "blj4_0Wg62kR",
         "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: keras-rs in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (0.2.1)\n",
-            "Requirement already satisfied: keras in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (3.10.0)\n",
-            "Requirement already satisfied: ml-dtypes in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (0.5.1)\n",
-            "Requirement already satisfied: absl-py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.3.1)\n",
-            "Requirement already satisfied: numpy in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.1.3)\n",
-            "Requirement already satisfied: rich in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (14.0.0)\n",
-            "Requirement already satisfied: namex in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.1.0)\n",
-            "Requirement already satisfied: h5py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (3.14.0)\n",
-            "Requirement already satisfied: optree in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.16.0)\n",
-            "Requirement already satisfied: packaging in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (25.0)\n",
-            "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from optree->keras->keras-rs) (4.14.1)\n",
-            "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (3.0.0)\n",
-            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (2.19.2)\n",
-            "Requirement already satisfied: mdurl~=0.1 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->keras->keras-rs) (0.1.2)\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "!pip install keras-rs"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
         "id": "QeFXrN1962kT"
       },
-      "outputs": [
-        {
-          "ename": "ModuleNotFoundError",
-          "evalue": "No module named 'jax'",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
-            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m      3\u001b[39m os.environ[\u001b[33m\"\u001b[39m\u001b[33mKERAS_BACKEND\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# `\"tensorflow\"`/`\"torch\"`\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _tf_keras \u001b[38;5;28;01mas\u001b[39;00m _tf_keras\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_tf_keras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m callbacks \u001b[38;5;28;01mas\u001b[39;00m callbacks\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/activations/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;28;01mas\u001b[39;00m deserialize\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get \u001b[38;5;28;01mas\u001b[39;00m get\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m serialize \u001b[38;5;28;01mas\u001b[39;00m serialize\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtypes\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m celu\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m exponential\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/activations.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi_export\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/__init__.py:41\u001b[39m\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtensorflow\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     42\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mtorch\u001b[39m\u001b[33m\"\u001b[39m:\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m distribution_lib\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m image\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/core.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexperimental\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax_sparse\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjnp\u001b[39;00m\n",
-            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'jax'"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "import os\n",
         "\n",
@@ -336,113 +293,7 @@
         "id": "kipGDLuZ62kW",
         "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "WARNING:absl:Variant folder /root/tensorflow_datasets/movielens/100k-ratings/0.1.1 has no dataset_info.json\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "58dfd53b556843dabaecd6cf2d6935ef",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Dl Completed...: 0 url [00:00, ? url/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "f3c1148efd194daeae69b8dbcc24c9d5",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "dc7d6718b55b499ebde1bd8c3b77c108",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Extraction completed...: 0 file [00:00, ? file/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "024cbbb854c943319b9be0d743e09f84",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6c26c23d5e9544e8b114c816ad667320",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Generating train examples...: 0 examples [00:00, ? examples/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ec2a121d9157485d9002795e0c86d471",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Dataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1. Subsequent calls will reuse this data.\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
         "ratings_ds = ratings_ds.map(\n",
@@ -642,76 +493,7 @@
         "id": "OAoTyyzi_Nau",
         "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - loss: 8.1493 - root_mean_squared_error: 2.8203\n",
-            "Epoch 2/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 2.8909 - root_mean_squared_error: 1.6905\n",
-            "Epoch 3/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5062 - root_mean_squared_error: 1.2266\n",
-            "Epoch 4/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2285 - root_mean_squared_error: 1.1076\n",
-            "Epoch 5/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.9398 - root_mean_squared_error: 0.9694 \n",
-            "Epoch 6/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.8562 - root_mean_squared_error: 0.9253\n",
-            "Epoch 7/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.7850 - root_mean_squared_error: 0.8860\n",
-            "Epoch 8/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7260 - root_mean_squared_error: 0.8520 \n",
-            "Epoch 9/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6800 - root_mean_squared_error: 0.8246 \n",
-            "Epoch 10/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.6444 - root_mean_squared_error: 0.8028\n",
-            "Epoch 11/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6183 - root_mean_squared_error: 0.7863 \n",
-            "Epoch 12/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5971 - root_mean_squared_error: 0.7727\n",
-            "Epoch 13/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5808 - root_mean_squared_error: 0.7621\n",
-            "Epoch 14/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5676 - root_mean_squared_error: 0.7534 \n",
-            "Epoch 15/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5566 - root_mean_squared_error: 0.7461\n",
-            "Epoch 16/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5474 - root_mean_squared_error: 0.7399\n",
-            "Epoch 17/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5399 - root_mean_squared_error: 0.7347 \n",
-            "Epoch 18/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5333 - root_mean_squared_error: 0.7303\n",
-            "Epoch 19/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5277 - root_mean_squared_error: 0.7264\n",
-            "Epoch 20/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5233 - root_mean_squared_error: 0.7234 \n",
-            "Epoch 21/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5191 - root_mean_squared_error: 0.7205 \n",
-            "Epoch 22/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5156 - root_mean_squared_error: 0.7180\n",
-            "Epoch 23/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5124 - root_mean_squared_error: 0.7158\n",
-            "Epoch 24/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5097 - root_mean_squared_error: 0.7139\n",
-            "Epoch 25/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5069 - root_mean_squared_error: 0.7120 \n",
-            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 513ms/step - loss: 0.6062 - root_mean_squared_error: 0.7786\n",
-            "Dot Network: RMSE = 0.7789321541786194; #params = 105025\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1200x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "dot_network = DLRM(\n",
         "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
@@ -783,35 +565,7 @@
         "id": "NH-VtGrMJtAt",
         "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Visualizing the feature interaction strengths:\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<Figure size 900x900 with 0 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 480x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Get the list of feature names in the correct order\n",
         "feature_names = list(vocabularies.keys())\n",

From 3f436fd8caa087ed93b86671032919f98e12c118 Mon Sep 17 00:00:00 2001
From: kharshith-k <kharshith@google.com>
Date: Mon, 11 Aug 2025 21:55:45 +0530
Subject: [PATCH 09/11] Delete examples/keras_rs/ipynb/dlrm.ipynb

Delete duplicate dlrm.ipynb
---
 examples/keras_rs/ipynb/dlrm.ipynb | 2962 ----------------------------
 1 file changed, 2962 deletions(-)
 delete mode 100644 examples/keras_rs/ipynb/dlrm.ipynb

diff --git a/examples/keras_rs/ipynb/dlrm.ipynb b/examples/keras_rs/ipynb/dlrm.ipynb
deleted file mode 100644
index 1fac111368..0000000000
--- a/examples/keras_rs/ipynb/dlrm.ipynb
+++ /dev/null
@@ -1,2962 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "33c74727"
-      },
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This tutorial demonstrates how to use the Deep Learning Recommendation Model (DLRM) to effectively learn the relationships between items and user preferences using a dot-product interaction mechanism. For more details, please refer to the [DLRM](https://arxiv.org/pdf/1906.00091) paper.\n",
-        "\n",
-        "DLRM is designed to excel at capturing explicit, bounded-degree feature interactions and is particularly effective at processing both categorical and continuous (sparse/dense) input features. The architecture consists of three main components: dedicated input layers to handle diverse features (typically embedding layers for categorical features), a dot-product interaction layer to explicitly model feature interactions, and a Multi-Layer Perceptron (MLP) to capture implicit feature relationships.\n",
-        "\n",
-        "The dot-product interaction layer lies at the heart of DLRM, efficiently computing pairwise interactions between different feature embeddings. This contrasts with models like Deep & Cross Network (DCN), which can treat elements within a feature vector as independent units, potentially leading to a higher-dimensional space and increased computational cost. The MLP is a standard feedforward network. The DLRM is formed by combining the interaction layer and MLP.\n",
-        "\n",
-        "The following image illustrates the DLRM architecture:\n",
-        "\n",
-        "![DLRM Architecture](https://raw.githubusercontent.com/kharshith-k/keras-io/refs/heads/keras-rs-examples/examples/keras_rs/img/dlrm/dlrm_architecture.gif)\n",
-        "\n",
-        "\n",
-        "Now that we have a foundational understanding of DLRM's architecture and key characteristics, let's dive into the code. We will train a DLRM on a real-world dataset to demonstrate its capability to learn meaningful feature interactions. Let's begin by setting the backend to JAX and organizing our imports."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "blj4_0Wg62kR",
-        "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: keras-rs in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (0.2.1)\n",
-            "Requirement already satisfied: keras in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (3.10.0)\n",
-            "Requirement already satisfied: ml-dtypes in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras-rs) (0.5.1)\n",
-            "Requirement already satisfied: absl-py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.3.1)\n",
-            "Requirement already satisfied: numpy in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (2.1.3)\n",
-            "Requirement already satisfied: rich in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (14.0.0)\n",
-            "Requirement already satisfied: namex in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.1.0)\n",
-            "Requirement already satisfied: h5py in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (3.14.0)\n",
-            "Requirement already satisfied: optree in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (0.16.0)\n",
-            "Requirement already satisfied: packaging in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from keras->keras-rs) (25.0)\n",
-            "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from optree->keras->keras-rs) (4.14.1)\n",
-            "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (3.0.0)\n",
-            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from rich->keras->keras-rs) (2.19.2)\n",
-            "Requirement already satisfied: mdurl~=0.1 in /Users/kharshith/keras-io/.venv/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->keras->keras-rs) (0.1.2)\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install keras-rs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "QeFXrN1962kT"
-      },
-      "outputs": [
-        {
-          "ename": "ModuleNotFoundError",
-          "evalue": "No module named 'jax'",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
-            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m      3\u001b[39m os.environ[\u001b[33m\"\u001b[39m\u001b[33mKERAS_BACKEND\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# `\"tensorflow\"`/`\"torch\"`\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _tf_keras \u001b[38;5;28;01mas\u001b[39;00m _tf_keras\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_tf_keras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/_tf_keras/keras/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations \u001b[38;5;28;01mas\u001b[39;00m activations\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications \u001b[38;5;28;01mas\u001b[39;00m applications\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m callbacks \u001b[38;5;28;01mas\u001b[39;00m callbacks\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/activations/__init__.py:7\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[33;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[33;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;28;01mas\u001b[39;00m deserialize\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get \u001b[38;5;28;01mas\u001b[39;00m get\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m serialize \u001b[38;5;28;01mas\u001b[39;00m serialize\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtypes\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m celu\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mactivations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m exponential\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/activations/activations.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi_export\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/__init__.py:41\u001b[39m\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtensorflow\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mjax\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     42\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Variable \u001b[38;5;28;01mas\u001b[39;00m BackendVariable\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m backend() == \u001b[33m\"\u001b[39m\u001b[33mtorch\u001b[39m\u001b[33m\"\u001b[39m:\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m distribution_lib\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackend\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mjax\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m image\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/keras-io/.venv/lib/python3.12/site-packages/keras/src/backend/jax/core.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexperimental\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax_sparse\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjax\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjnp\u001b[39;00m\n",
-            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'jax'"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "\n",
-        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # `\"tensorflow\"`/`\"torch\"`\n",
-        "\n",
-        "import keras\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "import tensorflow as tf\n",
-        "import tensorflow_datasets as tfds\n",
-        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
-        "\n",
-        "import keras_rs"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gHdT0D1762kT"
-      },
-      "source": [
-        "Let's also define variables which will be reused throughout the example."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "GRJOAiMv62kT"
-      },
-      "outputs": [],
-      "source": [
-        "MOVIELENS_CONFIG = {\n",
-        "    # features\n",
-        "    \"int_features\": [\n",
-        "        \"movie_id\",\n",
-        "        \"user_id\",\n",
-        "        \"user_gender\",\n",
-        "        \"bucketized_user_age\",\n",
-        "    ],\n",
-        "    \"str_features\": [\n",
-        "        \"user_zip_code\",\n",
-        "        \"user_occupation_text\",\n",
-        "    ],\n",
-        "    # model\n",
-        "    \"embedding_dim\": 8,\n",
-        "    \"deep_net_num_units\": [192, 192, 192],\n",
-        "    # training\n",
-        "    \"learning_rate\": 1e-2,\n",
-        "    \"num_epochs\": 25,\n",
-        "    \"batch_size\": 8192,\n",
-        "}\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_6J6XFNC62kT"
-      },
-      "source": [
-        "Here, we define a helper function for visualising weights of the cross layer in\n",
-        "order to better understand its functioning. Also, we define a function for\n",
-        "compiling, training and evaluating a given model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "jyojSTKw62kT"
-      },
-      "outputs": [],
-      "source": [
-        "def plot_training_metrics(history):\n",
-        "    \"\"\"Graphs all metrics tracked in the history object.\"\"\"\n",
-        "    plt.figure(figsize=(12, 6))\n",
-        "\n",
-        "    for metric_name, metric_values in history.history.items():\n",
-        "        plt.plot(metric_values, label=metric_name.replace('_', ' ').title())\n",
-        "\n",
-        "    plt.title('Metrics over Epochs')\n",
-        "    plt.xlabel('Epoch')\n",
-        "    plt.ylabel('Metric Value')\n",
-        "    plt.legend()\n",
-        "    plt.grid(True)\n",
-        "\n",
-        "def visualize_layer(matrix, features, cmap=plt.cm.Blues):\n",
-        "    plt.figure(figsize=(9, 9))\n",
-        "\n",
-        "    im = plt.matshow(matrix, cmap=cmap)\n",
-        "\n",
-        "    ax = plt.gca()\n",
-        "    divider = make_axes_locatable(plt.gca())\n",
-        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
-        "    plt.colorbar(im, cax=cax)\n",
-        "    cax.tick_params(labelsize=10)\n",
-        "\n",
-        "    # Set tick locations explicitly before setting labels\n",
-        "    ax.set_xticks(np.arange(len(features)))\n",
-        "    ax.set_yticks(np.arange(len(features)))\n",
-        "\n",
-        "    ax.set_xticklabels(features, rotation=45, fontsize=5)\n",
-        "    ax.set_yticklabels(features, fontsize=5)\n",
-        "\n",
-        "    plt.show()\n",
-        "\n",
-        "\n",
-        "def train_and_evaluate(\n",
-        "    learning_rate,\n",
-        "    epochs,\n",
-        "    train_data,\n",
-        "    test_data,\n",
-        "    model,\n",
-        "    plot_metrics=False,\n",
-        "):\n",
-        "    optimizer = keras.optimizers.AdamW(learning_rate=learning_rate)\n",
-        "    loss = keras.losses.MeanSquaredError()\n",
-        "    rmse = keras.metrics.RootMeanSquaredError()\n",
-        "\n",
-        "    model.compile(\n",
-        "        optimizer=optimizer,\n",
-        "        loss=loss,\n",
-        "        metrics=[rmse],\n",
-        "    )\n",
-        "\n",
-        "    history = model.fit(\n",
-        "        train_data,\n",
-        "        epochs=epochs,\n",
-        "        verbose=1,\n",
-        "    )\n",
-        "    if plot_metrics:\n",
-        "      plot_training_metrics(history)\n",
-        "\n",
-        "    results = model.evaluate(test_data, return_dict=True, verbose=1)\n",
-        "    rmse_value = results[\"root_mean_squared_error\"]\n",
-        "\n",
-        "    return rmse_value, model.count_params()\n",
-        "\n",
-        "\n",
-        "def print_stats(rmse_list, num_params, model_name):\n",
-        "    # Report metrics.\n",
-        "    num_trials = len(rmse_list)\n",
-        "    avg_rmse = np.mean(rmse_list)\n",
-        "    std_rmse = np.std(rmse_list)\n",
-        "\n",
-        "    if num_trials == 1:\n",
-        "        print(f\"{model_name}: RMSE = {avg_rmse}; #params = {num_params}\")\n",
-        "    else:\n",
-        "        print(f\"{model_name}: RMSE = {avg_rmse} ± {std_rmse}; #params = {num_params}\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tVHJBIJ_62kV"
-      },
-      "source": [
-        "## Real-world example\n",
-        "\n",
-        "Let's use the MovieLens 100K dataset. This dataset is used to train models to\n",
-        "predict users' movie ratings, based on user-related features and movie-related\n",
-        "features.\n",
-        "\n",
-        "### Preparing the dataset\n",
-        "\n",
-        "The dataset processing steps here are similar to what's given in the\n",
-        "[basic ranking](/keras_rs/examples/basic_ranking/)\n",
-        "tutorial. Let's load the dataset, and keep only the useful columns."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 187,
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-        "id": "kipGDLuZ62kW",
-        "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "WARNING:absl:Variant folder /root/tensorflow_datasets/movielens/100k-ratings/0.1.1 has no dataset_info.json\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "58dfd53b556843dabaecd6cf2d6935ef",
-              "version_major": 2,
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-            "text/plain": [
-              "Dl Completed...: 0 url [00:00, ? url/s]"
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-          "metadata": {},
-          "output_type": "display_data"
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-              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
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-          "metadata": {},
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-              "Extraction completed...: 0 file [00:00, ? file/s]"
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-          "metadata": {},
-          "output_type": "display_data"
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-        {
-          "data": {
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-              "model_id": "024cbbb854c943319b9be0d743e09f84",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
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-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6c26c23d5e9544e8b114c816ad667320",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Generating train examples...: 0 examples [00:00, ? examples/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ec2a121d9157485d9002795e0c86d471",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Shuffling /root/tensorflow_datasets/movielens/100k-ratings/incomplete.S3UIO5_0.1.1/movielens-train.tfrecord*..…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Dataset movielens downloaded and prepared to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1. Subsequent calls will reuse this data.\n"
-          ]
-        }
-      ],
-      "source": [
-        "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
-        "ratings_ds = ratings_ds.map(\n",
-        "    lambda x: (\n",
-        "        {\n",
-        "            \"movie_id\": int(x[\"movie_id\"]),\n",
-        "            \"user_id\": int(x[\"user_id\"]),\n",
-        "            \"user_gender\": int(x[\"user_gender\"]),\n",
-        "            \"user_zip_code\": x[\"user_zip_code\"],\n",
-        "            \"user_occupation_text\": x[\"user_occupation_text\"],\n",
-        "            \"bucketized_user_age\": int(x[\"bucketized_user_age\"]),\n",
-        "        },\n",
-        "        x[\"user_rating\"],  # label\n",
-        "    )\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3DrNFuZi62kW"
-      },
-      "source": [
-        "For every feature, let's get the list of unique values, i.e., vocabulary, so\n",
-        "that we can use that for the embedding layer."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pczAh0MV62kW"
-      },
-      "outputs": [],
-      "source": [
-        "vocabularies = {}\n",
-        "for feature_name in MOVIELENS_CONFIG[\"int_features\"] + MOVIELENS_CONFIG[\"str_features\"]:\n",
-        "    vocabulary = ratings_ds.batch(10_000).map(lambda x, y: x[feature_name])\n",
-        "    vocabularies[feature_name] = np.unique(np.concatenate(list(vocabulary)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1PLhYqF062kW"
-      },
-      "source": [
-        "One thing we need to do is to use `keras.layers.StringLookup` and\n",
-        "`keras.layers.IntegerLookup` to convert all features into indices, which can\n",
-        "then be fed into embedding layers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pLMvMAIF62kW"
-      },
-      "outputs": [],
-      "source": [
-        "lookup_layers = {}\n",
-        "lookup_layers.update(\n",
-        "    {\n",
-        "        feature: keras.layers.IntegerLookup(vocabulary=vocabularies[feature])\n",
-        "        for feature in MOVIELENS_CONFIG[\"int_features\"]\n",
-        "    }\n",
-        ")\n",
-        "lookup_layers.update(\n",
-        "    {\n",
-        "        feature: keras.layers.StringLookup(vocabulary=vocabularies[feature])\n",
-        "        for feature in MOVIELENS_CONFIG[\"str_features\"]\n",
-        "    }\n",
-        ")\n",
-        "\n",
-        "ratings_ds = ratings_ds.map(\n",
-        "    lambda x, y: (\n",
-        "        {\n",
-        "            feature_name: lookup_layers[feature_name](x[feature_name])\n",
-        "            for feature_name in vocabularies\n",
-        "        },\n",
-        "        y,\n",
-        "    )\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XBKk-mX962kW"
-      },
-      "source": [
-        "Let's split our data into train and test sets. We also use `cache()` and\n",
-        "`prefetch()` for better performance."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Ha3v4rHN62kW"
-      },
-      "outputs": [],
-      "source": [
-        "ratings_ds = ratings_ds.shuffle(100_000)\n",
-        "\n",
-        "train_ds = (\n",
-        "    ratings_ds.take(80_000)\n",
-        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
-        "    .cache()\n",
-        "    .prefetch(tf.data.AUTOTUNE)\n",
-        ")\n",
-        "test_ds = (\n",
-        "    ratings_ds.skip(80_000)\n",
-        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
-        "    .take(20_000)\n",
-        "    .cache()\n",
-        "    .prefetch(tf.data.AUTOTUNE)\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RXuXvsl062kW"
-      },
-      "source": [
-        "### Building the model\n",
-        "\n",
-        "The model will have embedding layers, followed by DotInteraction and feedforward\n",
-        "layers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ww3ZJ6R062kW"
-      },
-      "outputs": [],
-      "source": [
-        "class DLRM(keras.Model):\n",
-        "    def __init__(\n",
-        "        self,\n",
-        "        dense_num_units_lst,\n",
-        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
-        "        **kwargs,\n",
-        "    ):\n",
-        "        super().__init__(**kwargs)\n",
-        "\n",
-        "        # Layers.\n",
-        "\n",
-        "        self.embedding_layers = []\n",
-        "        for feature_name, vocabulary in vocabularies.items():\n",
-        "            self.embedding_layers.append(\n",
-        "                keras.layers.Embedding(\n",
-        "                    input_dim=len(vocabulary) + 1,\n",
-        "                    output_dim=embedding_dim,\n",
-        "                )\n",
-        "            )\n",
-        "\n",
-        "        self.dot_layer = keras_rs.layers.DotInteraction()\n",
-        "\n",
-        "        self.dense_layers = []\n",
-        "        for num_units in dense_num_units_lst:\n",
-        "            self.dense_layers.append(keras.layers.Dense(num_units, activation=\"relu\"))\n",
-        "\n",
-        "        self.output_layer = keras.layers.Dense(1)\n",
-        "\n",
-        "        # Attributes.\n",
-        "        self.dense_num_units_lst = dense_num_units_lst\n",
-        "        self.embedding_dim = embedding_dim\n",
-        "\n",
-        "    def call(self, inputs):\n",
-        "        embeddings = []\n",
-        "        for feature_name, embedding_layer in zip(vocabularies, self.embedding_layers):\n",
-        "            embeddings.append(embedding_layer(inputs[feature_name]))\n",
-        "\n",
-        "        # Pass the list of embeddings to the DotInteraction layer\n",
-        "        x = self.dot_layer(embeddings)\n",
-        "\n",
-        "        for dense_layer in self.dense_layers:\n",
-        "            x = dense_layer(x)\n",
-        "\n",
-        "        x = self.output_layer(x)\n",
-        "\n",
-        "        return x"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "OAoTyyzi_Nau",
-        "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1s/step - loss: 8.1493 - root_mean_squared_error: 2.8203\n",
-            "Epoch 2/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 2.8909 - root_mean_squared_error: 1.6905\n",
-            "Epoch 3/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5062 - root_mean_squared_error: 1.2266\n",
-            "Epoch 4/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2285 - root_mean_squared_error: 1.1076\n",
-            "Epoch 5/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.9398 - root_mean_squared_error: 0.9694 \n",
-            "Epoch 6/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.8562 - root_mean_squared_error: 0.9253\n",
-            "Epoch 7/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.7850 - root_mean_squared_error: 0.8860\n",
-            "Epoch 8/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7260 - root_mean_squared_error: 0.8520 \n",
-            "Epoch 9/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6800 - root_mean_squared_error: 0.8246 \n",
-            "Epoch 10/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.6444 - root_mean_squared_error: 0.8028\n",
-            "Epoch 11/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6183 - root_mean_squared_error: 0.7863 \n",
-            "Epoch 12/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5971 - root_mean_squared_error: 0.7727\n",
-            "Epoch 13/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5808 - root_mean_squared_error: 0.7621\n",
-            "Epoch 14/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5676 - root_mean_squared_error: 0.7534 \n",
-            "Epoch 15/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5566 - root_mean_squared_error: 0.7461\n",
-            "Epoch 16/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5474 - root_mean_squared_error: 0.7399\n",
-            "Epoch 17/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5399 - root_mean_squared_error: 0.7347 \n",
-            "Epoch 18/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5333 - root_mean_squared_error: 0.7303\n",
-            "Epoch 19/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5277 - root_mean_squared_error: 0.7264\n",
-            "Epoch 20/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5233 - root_mean_squared_error: 0.7234 \n",
-            "Epoch 21/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5191 - root_mean_squared_error: 0.7205 \n",
-            "Epoch 22/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5156 - root_mean_squared_error: 0.7180\n",
-            "Epoch 23/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5124 - root_mean_squared_error: 0.7158\n",
-            "Epoch 24/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.5097 - root_mean_squared_error: 0.7139\n",
-            "Epoch 25/25\n",
-            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5069 - root_mean_squared_error: 0.7120 \n",
-            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 513ms/step - loss: 0.6062 - root_mean_squared_error: 0.7786\n",
-            "Dot Network: RMSE = 0.7789321541786194; #params = 105025\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1200x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "dot_network = DLRM(\n",
-        "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
-        "    embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
-        ")\n",
-        "rmse, dot_network_num_params = train_and_evaluate(\n",
-        "    learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
-        "    epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
-        "    train_data=train_ds,\n",
-        "    test_data=test_ds,\n",
-        "    model= dot_network,\n",
-        "    plot_metrics=True\n",
-        ")\n",
-        "print_stats(\n",
-        "    rmse_list=[rmse],\n",
-        "    num_params=dot_network_num_params,\n",
-        "    model_name=\"Dot Network\",\n",
-        ")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E68GrcDN62kW"
-      },
-      "source": [
-        "### Visualizing feature interactions\n",
-        "\n",
-        "The DotInteraction layer itself doesn't have a conventional \"weight\" matrix like a Dense layer. Instead, its function is to compute the dot product between the embedding vectors of your features.\n",
-        "\n",
-        "To visualize the strength of these interactions, we can calculate a matrix representing the pairwise interaction strength between all feature embeddings. A common way to do this is to take the dot product of the embedding matrices for each pair of features and then aggregate the result into a single value (like the mean of the absolute values) that represents the overall interaction strength."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "R8WPVVn-Jq81"
-      },
-      "outputs": [],
-      "source": [
-        "def get_dot_interaction_matrix(model, feature_names):\n",
-        "    # Extract the trained embedding weights from the model\n",
-        "    embedding_weights = [layer.get_weights()[0] for layer in model.embedding_layers]\n",
-        "\n",
-        "    num_features = len(feature_names)\n",
-        "    interaction_matrix = np.zeros((num_features, num_features))\n",
-        "\n",
-        "    # Iterate through each pair of features to calculate their interaction strength\n",
-        "    for i in range(num_features):\n",
-        "        for j in range(num_features):\n",
-        "            # Calculate the dot product between the two embedding matrices\n",
-        "            interaction = np.dot(embedding_weights[i], embedding_weights[j].T)\n",
-        "            # Take the mean of the absolute values as a measure of interaction strength\n",
-        "            interaction_strength = np.mean(np.abs(interaction))\n",
-        "            interaction_matrix[i, j] = interaction_strength\n",
-        "\n",
-        "    return interaction_matrix"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 512
-        },
-        "id": "NH-VtGrMJtAt",
-        "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Visualizing the feature interaction strengths:\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<Figure size 900x900 with 0 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 480x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Get the list of feature names in the correct order\n",
-        "feature_names = list(vocabularies.keys())\n",
-        "\n",
-        "# Calculate the interaction matrix\n",
-        "interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)\n",
-        "\n",
-        "# Visualize the matrix as a heatmap\n",
-        "print(\"\\nVisualizing the feature interaction strengths:\")\n",
-        "visualize_layer(interaction_matrix, feature_names)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IbpjtvnQ62kW"
-      },
-      "outputs": [],
-      "source": [
-        "dlrm_rmse_list = []\n",
-        "\n",
-        "for _ in range(20):\n",
-        "\n",
-        "    dot_network = DLRM(\n",
-        "        dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
-        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
-        "    )\n",
-        "    rmse, dot_network_num_params = train_and_evaluate(\n",
-        "        learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
-        "        epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
-        "        train_data=train_ds,\n",
-        "        test_data=test_ds,\n",
-        "        model= dot_network,\n",
-        "    )\n",
-        "    dlrm_rmse_list.append(rmse)\n",
-        "\n",
-        "print_stats(\n",
-        "    rmse_list=dlrm_rmse_list,\n",
-        "    num_params=dot_network_num_params,\n",
-        "    model_name=\"Dot Network\",\n",
-        ")\n",
-        "\n"
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-        "def plot_rmse(rmse_list, model_name):\n",
-        "  plt.figure()\n",
-        "  plt.plot(rmse_list)\n",
-        "  plt.title(f'RMSE over trials for {model_name}')\n",
-        "  plt.xlabel('Trial')\n",
-        "  plt.ylabel('RMSE')\n",
-        "  plt.show()\n",
-        "\n",
-        "plot_rmse(dlrm_rmse_list, \"Dot Network\")"
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From c875a722e7c1e85a1f307ca8a4e8d7806a2f6551 Mon Sep 17 00:00:00 2001
From: Harshith K <kharshith@google.com>
Date: Tue, 12 Aug 2025 09:57:08 +0530
Subject: [PATCH 10/11] removed DLRM.ipynb

---
 examples/keras_rs/ipynb/DLRM.ipynb | 2716 ----------------------------
 1 file changed, 2716 deletions(-)
 delete mode 100644 examples/keras_rs/ipynb/DLRM.ipynb

diff --git a/examples/keras_rs/ipynb/DLRM.ipynb b/examples/keras_rs/ipynb/DLRM.ipynb
deleted file mode 100644
index c0b410ae69..0000000000
--- a/examples/keras_rs/ipynb/DLRM.ipynb
+++ /dev/null
@@ -1,2716 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "33c74727"
-      },
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This tutorial demonstrates how to use the Deep Learning Recommendation Model (DLRM) to effectively learn the relationships between items and user preferences using a dot-product interaction mechanism. For more details, please refer to the [DLRM](https://arxiv.org/pdf/1906.00091) paper.\n",
-        "\n",
-        "DLRM is designed to excel at capturing explicit, bounded-degree feature interactions and is particularly effective at processing both categorical and continuous (sparse/dense) input features. The architecture consists of three main components: dedicated input layers to handle diverse features (typically embedding layers for categorical features), a dot-product interaction layer to explicitly model feature interactions, and a Multi-Layer Perceptron (MLP) to capture implicit feature relationships.\n",
-        "\n",
-        "The dot-product interaction layer lies at the heart of DLRM, efficiently computing pairwise interactions between different feature embeddings. This contrasts with models like Deep & Cross Network (DCN), which can treat elements within a feature vector as independent units, potentially leading to a higher-dimensional space and increased computational cost. The MLP is a standard feedforward network. The DLRM is formed by combining the interaction layer and MLP.\n",
-        "\n",
-        "The following image illustrates the DLRM architecture:\n",
-        "\n",
-        "![DLRM Architecture](https://raw.githubusercontent.com/kharshith-k/keras-io/refs/heads/keras-rs-examples/examples/keras_rs/img/dlrm/dlrm_architecture.gif)\n",
-        "\n",
-        "\n",
-        "Now that we have a foundational understanding of DLRM's architecture and key characteristics, let's dive into the code. We will train a DLRM on a real-world dataset to demonstrate its capability to learn meaningful feature interactions. Let's begin by setting the backend to JAX and organizing our imports."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "blj4_0Wg62kR",
-        "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install keras-rs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "QeFXrN1962kT"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # `\"tensorflow\"`/`\"torch\"`\n",
-        "\n",
-        "import keras\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "import tensorflow as tf\n",
-        "import tensorflow_datasets as tfds\n",
-        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
-        "\n",
-        "import keras_rs"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gHdT0D1762kT"
-      },
-      "source": [
-        "Let's also define variables which will be reused throughout the example."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "GRJOAiMv62kT"
-      },
-      "outputs": [],
-      "source": [
-        "MOVIELENS_CONFIG = {\n",
-        "    # features\n",
-        "    \"int_features\": [\n",
-        "        \"movie_id\",\n",
-        "        \"user_id\",\n",
-        "        \"user_gender\",\n",
-        "        \"bucketized_user_age\",\n",
-        "    ],\n",
-        "    \"str_features\": [\n",
-        "        \"user_zip_code\",\n",
-        "        \"user_occupation_text\",\n",
-        "    ],\n",
-        "    # model\n",
-        "    \"embedding_dim\": 8,\n",
-        "    \"deep_net_num_units\": [192, 192, 192],\n",
-        "    # training\n",
-        "    \"learning_rate\": 1e-2,\n",
-        "    \"num_epochs\": 25,\n",
-        "    \"batch_size\": 8192,\n",
-        "}\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_6J6XFNC62kT"
-      },
-      "source": [
-        "Here, we define a helper function for visualising weights of the cross layer in\n",
-        "order to better understand its functioning. Also, we define a function for\n",
-        "compiling, training and evaluating a given model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "jyojSTKw62kT"
-      },
-      "outputs": [],
-      "source": [
-        "def plot_training_metrics(history):\n",
-        "    \"\"\"Graphs all metrics tracked in the history object.\"\"\"\n",
-        "    plt.figure(figsize=(12, 6))\n",
-        "\n",
-        "    for metric_name, metric_values in history.history.items():\n",
-        "        plt.plot(metric_values, label=metric_name.replace('_', ' ').title())\n",
-        "\n",
-        "    plt.title('Metrics over Epochs')\n",
-        "    plt.xlabel('Epoch')\n",
-        "    plt.ylabel('Metric Value')\n",
-        "    plt.legend()\n",
-        "    plt.grid(True)\n",
-        "\n",
-        "def visualize_layer(matrix, features, cmap=plt.cm.Blues):\n",
-        "    plt.figure(figsize=(9, 9))\n",
-        "\n",
-        "    im = plt.matshow(matrix, cmap=cmap)\n",
-        "\n",
-        "    ax = plt.gca()\n",
-        "    divider = make_axes_locatable(plt.gca())\n",
-        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
-        "    plt.colorbar(im, cax=cax)\n",
-        "    cax.tick_params(labelsize=10)\n",
-        "\n",
-        "    # Set tick locations explicitly before setting labels\n",
-        "    ax.set_xticks(np.arange(len(features)))\n",
-        "    ax.set_yticks(np.arange(len(features)))\n",
-        "\n",
-        "    ax.set_xticklabels(features, rotation=45, fontsize=5)\n",
-        "    ax.set_yticklabels(features, fontsize=5)\n",
-        "\n",
-        "    plt.show()\n",
-        "\n",
-        "\n",
-        "def train_and_evaluate(\n",
-        "    learning_rate,\n",
-        "    epochs,\n",
-        "    train_data,\n",
-        "    test_data,\n",
-        "    model,\n",
-        "    plot_metrics=False,\n",
-        "):\n",
-        "    optimizer = keras.optimizers.AdamW(learning_rate=learning_rate)\n",
-        "    loss = keras.losses.MeanSquaredError()\n",
-        "    rmse = keras.metrics.RootMeanSquaredError()\n",
-        "\n",
-        "    model.compile(\n",
-        "        optimizer=optimizer,\n",
-        "        loss=loss,\n",
-        "        metrics=[rmse],\n",
-        "    )\n",
-        "\n",
-        "    history = model.fit(\n",
-        "        train_data,\n",
-        "        epochs=epochs,\n",
-        "        verbose=1,\n",
-        "    )\n",
-        "    if plot_metrics:\n",
-        "      plot_training_metrics(history)\n",
-        "\n",
-        "    results = model.evaluate(test_data, return_dict=True, verbose=1)\n",
-        "    rmse_value = results[\"root_mean_squared_error\"]\n",
-        "\n",
-        "    return rmse_value, model.count_params()\n",
-        "\n",
-        "\n",
-        "def print_stats(rmse_list, num_params, model_name):\n",
-        "    # Report metrics.\n",
-        "    num_trials = len(rmse_list)\n",
-        "    avg_rmse = np.mean(rmse_list)\n",
-        "    std_rmse = np.std(rmse_list)\n",
-        "\n",
-        "    if num_trials == 1:\n",
-        "        print(f\"{model_name}: RMSE = {avg_rmse}; #params = {num_params}\")\n",
-        "    else:\n",
-        "        print(f\"{model_name}: RMSE = {avg_rmse} ± {std_rmse}; #params = {num_params}\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tVHJBIJ_62kV"
-      },
-      "source": [
-        "## Real-world example\n",
-        "\n",
-        "Let's use the MovieLens 100K dataset. This dataset is used to train models to\n",
-        "predict users' movie ratings, based on user-related features and movie-related\n",
-        "features.\n",
-        "\n",
-        "### Preparing the dataset\n",
-        "\n",
-        "The dataset processing steps here are similar to what's given in the\n",
-        "[basic ranking](/keras_rs/examples/basic_ranking/)\n",
-        "tutorial. Let's load the dataset, and keep only the useful columns."
-      ]
-    },
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-        },
-        "id": "kipGDLuZ62kW",
-        "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
-      },
-      "outputs": [],
-      "source": [
-        "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
-        "ratings_ds = ratings_ds.map(\n",
-        "    lambda x: (\n",
-        "        {\n",
-        "            \"movie_id\": int(x[\"movie_id\"]),\n",
-        "            \"user_id\": int(x[\"user_id\"]),\n",
-        "            \"user_gender\": int(x[\"user_gender\"]),\n",
-        "            \"user_zip_code\": x[\"user_zip_code\"],\n",
-        "            \"user_occupation_text\": x[\"user_occupation_text\"],\n",
-        "            \"bucketized_user_age\": int(x[\"bucketized_user_age\"]),\n",
-        "        },\n",
-        "        x[\"user_rating\"],  # label\n",
-        "    )\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3DrNFuZi62kW"
-      },
-      "source": [
-        "For every feature, let's get the list of unique values, i.e., vocabulary, so\n",
-        "that we can use that for the embedding layer."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pczAh0MV62kW"
-      },
-      "outputs": [],
-      "source": [
-        "vocabularies = {}\n",
-        "for feature_name in MOVIELENS_CONFIG[\"int_features\"] + MOVIELENS_CONFIG[\"str_features\"]:\n",
-        "    vocabulary = ratings_ds.batch(10_000).map(lambda x, y: x[feature_name])\n",
-        "    vocabularies[feature_name] = np.unique(np.concatenate(list(vocabulary)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1PLhYqF062kW"
-      },
-      "source": [
-        "One thing we need to do is to use `keras.layers.StringLookup` and\n",
-        "`keras.layers.IntegerLookup` to convert all features into indices, which can\n",
-        "then be fed into embedding layers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pLMvMAIF62kW"
-      },
-      "outputs": [],
-      "source": [
-        "lookup_layers = {}\n",
-        "lookup_layers.update(\n",
-        "    {\n",
-        "        feature: keras.layers.IntegerLookup(vocabulary=vocabularies[feature])\n",
-        "        for feature in MOVIELENS_CONFIG[\"int_features\"]\n",
-        "    }\n",
-        ")\n",
-        "lookup_layers.update(\n",
-        "    {\n",
-        "        feature: keras.layers.StringLookup(vocabulary=vocabularies[feature])\n",
-        "        for feature in MOVIELENS_CONFIG[\"str_features\"]\n",
-        "    }\n",
-        ")\n",
-        "\n",
-        "ratings_ds = ratings_ds.map(\n",
-        "    lambda x, y: (\n",
-        "        {\n",
-        "            feature_name: lookup_layers[feature_name](x[feature_name])\n",
-        "            for feature_name in vocabularies\n",
-        "        },\n",
-        "        y,\n",
-        "    )\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XBKk-mX962kW"
-      },
-      "source": [
-        "Let's split our data into train and test sets. We also use `cache()` and\n",
-        "`prefetch()` for better performance."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Ha3v4rHN62kW"
-      },
-      "outputs": [],
-      "source": [
-        "ratings_ds = ratings_ds.shuffle(100_000)\n",
-        "\n",
-        "train_ds = (\n",
-        "    ratings_ds.take(80_000)\n",
-        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
-        "    .cache()\n",
-        "    .prefetch(tf.data.AUTOTUNE)\n",
-        ")\n",
-        "test_ds = (\n",
-        "    ratings_ds.skip(80_000)\n",
-        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
-        "    .take(20_000)\n",
-        "    .cache()\n",
-        "    .prefetch(tf.data.AUTOTUNE)\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RXuXvsl062kW"
-      },
-      "source": [
-        "### Building the model\n",
-        "\n",
-        "The model will have embedding layers, followed by DotInteraction and feedforward\n",
-        "layers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ww3ZJ6R062kW"
-      },
-      "outputs": [],
-      "source": [
-        "class DLRM(keras.Model):\n",
-        "    def __init__(\n",
-        "        self,\n",
-        "        dense_num_units_lst,\n",
-        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
-        "        **kwargs,\n",
-        "    ):\n",
-        "        super().__init__(**kwargs)\n",
-        "\n",
-        "        # Layers.\n",
-        "\n",
-        "        self.embedding_layers = []\n",
-        "        for feature_name, vocabulary in vocabularies.items():\n",
-        "            self.embedding_layers.append(\n",
-        "                keras.layers.Embedding(\n",
-        "                    input_dim=len(vocabulary) + 1,\n",
-        "                    output_dim=embedding_dim,\n",
-        "                )\n",
-        "            )\n",
-        "\n",
-        "        self.dot_layer = keras_rs.layers.DotInteraction()\n",
-        "\n",
-        "        self.dense_layers = []\n",
-        "        for num_units in dense_num_units_lst:\n",
-        "            self.dense_layers.append(keras.layers.Dense(num_units, activation=\"relu\"))\n",
-        "\n",
-        "        self.output_layer = keras.layers.Dense(1)\n",
-        "\n",
-        "        # Attributes.\n",
-        "        self.dense_num_units_lst = dense_num_units_lst\n",
-        "        self.embedding_dim = embedding_dim\n",
-        "\n",
-        "    def call(self, inputs):\n",
-        "        embeddings = []\n",
-        "        for feature_name, embedding_layer in zip(vocabularies, self.embedding_layers):\n",
-        "            embeddings.append(embedding_layer(inputs[feature_name]))\n",
-        "\n",
-        "        # Pass the list of embeddings to the DotInteraction layer\n",
-        "        x = self.dot_layer(embeddings)\n",
-        "\n",
-        "        for dense_layer in self.dense_layers:\n",
-        "            x = dense_layer(x)\n",
-        "\n",
-        "        x = self.output_layer(x)\n",
-        "\n",
-        "        return x"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "OAoTyyzi_Nau",
-        "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
-      },
-      "outputs": [],
-      "source": [
-        "dot_network = DLRM(\n",
-        "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
-        "    embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
-        ")\n",
-        "rmse, dot_network_num_params = train_and_evaluate(\n",
-        "    learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
-        "    epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
-        "    train_data=train_ds,\n",
-        "    test_data=test_ds,\n",
-        "    model= dot_network,\n",
-        "    plot_metrics=True\n",
-        ")\n",
-        "print_stats(\n",
-        "    rmse_list=[rmse],\n",
-        "    num_params=dot_network_num_params,\n",
-        "    model_name=\"Dot Network\",\n",
-        ")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E68GrcDN62kW"
-      },
-      "source": [
-        "### Visualizing feature interactions\n",
-        "\n",
-        "The DotInteraction layer itself doesn't have a conventional \"weight\" matrix like a Dense layer. Instead, its function is to compute the dot product between the embedding vectors of your features.\n",
-        "\n",
-        "To visualize the strength of these interactions, we can calculate a matrix representing the pairwise interaction strength between all feature embeddings. A common way to do this is to take the dot product of the embedding matrices for each pair of features and then aggregate the result into a single value (like the mean of the absolute values) that represents the overall interaction strength."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "R8WPVVn-Jq81"
-      },
-      "outputs": [],
-      "source": [
-        "def get_dot_interaction_matrix(model, feature_names):\n",
-        "    # Extract the trained embedding weights from the model\n",
-        "    embedding_weights = [layer.get_weights()[0] for layer in model.embedding_layers]\n",
-        "\n",
-        "    num_features = len(feature_names)\n",
-        "    interaction_matrix = np.zeros((num_features, num_features))\n",
-        "\n",
-        "    # Iterate through each pair of features to calculate their interaction strength\n",
-        "    for i in range(num_features):\n",
-        "        for j in range(num_features):\n",
-        "            # Calculate the dot product between the two embedding matrices\n",
-        "            interaction = np.dot(embedding_weights[i], embedding_weights[j].T)\n",
-        "            # Take the mean of the absolute values as a measure of interaction strength\n",
-        "            interaction_strength = np.mean(np.abs(interaction))\n",
-        "            interaction_matrix[i, j] = interaction_strength\n",
-        "\n",
-        "    return interaction_matrix"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 512
-        },
-        "id": "NH-VtGrMJtAt",
-        "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
-      },
-      "outputs": [],
-      "source": [
-        "# Get the list of feature names in the correct order\n",
-        "feature_names = list(vocabularies.keys())\n",
-        "\n",
-        "# Calculate the interaction matrix\n",
-        "interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)\n",
-        "\n",
-        "# Visualize the matrix as a heatmap\n",
-        "print(\"\\nVisualizing the feature interaction strengths:\")\n",
-        "visualize_layer(interaction_matrix, feature_names)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IbpjtvnQ62kW"
-      },
-      "outputs": [],
-      "source": [
-        "dlrm_rmse_list = []\n",
-        "\n",
-        "for _ in range(20):\n",
-        "\n",
-        "    dot_network = DLRM(\n",
-        "        dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
-        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
-        "    )\n",
-        "    rmse, dot_network_num_params = train_and_evaluate(\n",
-        "        learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
-        "        epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
-        "        train_data=train_ds,\n",
-        "        test_data=test_ds,\n",
-        "        model= dot_network,\n",
-        "    )\n",
-        "    dlrm_rmse_list.append(rmse)\n",
-        "\n",
-        "print_stats(\n",
-        "    rmse_list=dlrm_rmse_list,\n",
-        "    num_params=dot_network_num_params,\n",
-        "    model_name=\"Dot Network\",\n",
-        ")\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8mskfVqa-uGx"
-      },
-      "outputs": [],
-      "source": [
-        "def plot_rmse(rmse_list, model_name):\n",
-        "  plt.figure()\n",
-        "  plt.plot(rmse_list)\n",
-        "  plt.title(f'RMSE over trials for {model_name}')\n",
-        "  plt.xlabel('Trial')\n",
-        "  plt.ylabel('RMSE')\n",
-        "  plt.show()\n",
-        "\n",
-        "plot_rmse(dlrm_rmse_list, \"Dot Network\")"
-      ]
-    }
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From d61e0241ddfa62c9c6a200f30dc4d0a7157bb184 Mon Sep 17 00:00:00 2001
From: Harshith K <kharshith@google.com>
Date: Tue, 12 Aug 2025 09:59:44 +0530
Subject: [PATCH 11/11] Removed duplicate of dlrm

---
 examples/keras_rs/ipynb/dlrm.ipynb | 2716 ++++++++++++++++++++++++++++
 1 file changed, 2716 insertions(+)
 create mode 100644 examples/keras_rs/ipynb/dlrm.ipynb

diff --git a/examples/keras_rs/ipynb/dlrm.ipynb b/examples/keras_rs/ipynb/dlrm.ipynb
new file mode 100644
index 0000000000..c0b410ae69
--- /dev/null
+++ b/examples/keras_rs/ipynb/dlrm.ipynb
@@ -0,0 +1,2716 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33c74727"
+      },
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This tutorial demonstrates how to use the Deep Learning Recommendation Model (DLRM) to effectively learn the relationships between items and user preferences using a dot-product interaction mechanism. For more details, please refer to the [DLRM](https://arxiv.org/pdf/1906.00091) paper.\n",
+        "\n",
+        "DLRM is designed to excel at capturing explicit, bounded-degree feature interactions and is particularly effective at processing both categorical and continuous (sparse/dense) input features. The architecture consists of three main components: dedicated input layers to handle diverse features (typically embedding layers for categorical features), a dot-product interaction layer to explicitly model feature interactions, and a Multi-Layer Perceptron (MLP) to capture implicit feature relationships.\n",
+        "\n",
+        "The dot-product interaction layer lies at the heart of DLRM, efficiently computing pairwise interactions between different feature embeddings. This contrasts with models like Deep & Cross Network (DCN), which can treat elements within a feature vector as independent units, potentially leading to a higher-dimensional space and increased computational cost. The MLP is a standard feedforward network. The DLRM is formed by combining the interaction layer and MLP.\n",
+        "\n",
+        "The following image illustrates the DLRM architecture:\n",
+        "\n",
+        "![DLRM Architecture](https://raw.githubusercontent.com/kharshith-k/keras-io/refs/heads/keras-rs-examples/examples/keras_rs/img/dlrm/dlrm_architecture.gif)\n",
+        "\n",
+        "\n",
+        "Now that we have a foundational understanding of DLRM's architecture and key characteristics, let's dive into the code. We will train a DLRM on a real-world dataset to demonstrate its capability to learn meaningful feature interactions. Let's begin by setting the backend to JAX and organizing our imports."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "blj4_0Wg62kR",
+        "outputId": "c386b17a-550c-4b21-dfc9-f7cf51b4a3a1"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install keras-rs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QeFXrN1962kT"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # `\"tensorflow\"`/`\"torch\"`\n",
+        "\n",
+        "import keras\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "import tensorflow_datasets as tfds\n",
+        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+        "\n",
+        "import keras_rs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gHdT0D1762kT"
+      },
+      "source": [
+        "Let's also define variables which will be reused throughout the example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GRJOAiMv62kT"
+      },
+      "outputs": [],
+      "source": [
+        "MOVIELENS_CONFIG = {\n",
+        "    # features\n",
+        "    \"int_features\": [\n",
+        "        \"movie_id\",\n",
+        "        \"user_id\",\n",
+        "        \"user_gender\",\n",
+        "        \"bucketized_user_age\",\n",
+        "    ],\n",
+        "    \"str_features\": [\n",
+        "        \"user_zip_code\",\n",
+        "        \"user_occupation_text\",\n",
+        "    ],\n",
+        "    # model\n",
+        "    \"embedding_dim\": 8,\n",
+        "    \"deep_net_num_units\": [192, 192, 192],\n",
+        "    # training\n",
+        "    \"learning_rate\": 1e-2,\n",
+        "    \"num_epochs\": 25,\n",
+        "    \"batch_size\": 8192,\n",
+        "}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_6J6XFNC62kT"
+      },
+      "source": [
+        "Here, we define a helper function for visualising weights of the cross layer in\n",
+        "order to better understand its functioning. Also, we define a function for\n",
+        "compiling, training and evaluating a given model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jyojSTKw62kT"
+      },
+      "outputs": [],
+      "source": [
+        "def plot_training_metrics(history):\n",
+        "    \"\"\"Graphs all metrics tracked in the history object.\"\"\"\n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "\n",
+        "    for metric_name, metric_values in history.history.items():\n",
+        "        plt.plot(metric_values, label=metric_name.replace('_', ' ').title())\n",
+        "\n",
+        "    plt.title('Metrics over Epochs')\n",
+        "    plt.xlabel('Epoch')\n",
+        "    plt.ylabel('Metric Value')\n",
+        "    plt.legend()\n",
+        "    plt.grid(True)\n",
+        "\n",
+        "def visualize_layer(matrix, features, cmap=plt.cm.Blues):\n",
+        "    plt.figure(figsize=(9, 9))\n",
+        "\n",
+        "    im = plt.matshow(matrix, cmap=cmap)\n",
+        "\n",
+        "    ax = plt.gca()\n",
+        "    divider = make_axes_locatable(plt.gca())\n",
+        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "    plt.colorbar(im, cax=cax)\n",
+        "    cax.tick_params(labelsize=10)\n",
+        "\n",
+        "    # Set tick locations explicitly before setting labels\n",
+        "    ax.set_xticks(np.arange(len(features)))\n",
+        "    ax.set_yticks(np.arange(len(features)))\n",
+        "\n",
+        "    ax.set_xticklabels(features, rotation=45, fontsize=5)\n",
+        "    ax.set_yticklabels(features, fontsize=5)\n",
+        "\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "def train_and_evaluate(\n",
+        "    learning_rate,\n",
+        "    epochs,\n",
+        "    train_data,\n",
+        "    test_data,\n",
+        "    model,\n",
+        "    plot_metrics=False,\n",
+        "):\n",
+        "    optimizer = keras.optimizers.AdamW(learning_rate=learning_rate)\n",
+        "    loss = keras.losses.MeanSquaredError()\n",
+        "    rmse = keras.metrics.RootMeanSquaredError()\n",
+        "\n",
+        "    model.compile(\n",
+        "        optimizer=optimizer,\n",
+        "        loss=loss,\n",
+        "        metrics=[rmse],\n",
+        "    )\n",
+        "\n",
+        "    history = model.fit(\n",
+        "        train_data,\n",
+        "        epochs=epochs,\n",
+        "        verbose=1,\n",
+        "    )\n",
+        "    if plot_metrics:\n",
+        "      plot_training_metrics(history)\n",
+        "\n",
+        "    results = model.evaluate(test_data, return_dict=True, verbose=1)\n",
+        "    rmse_value = results[\"root_mean_squared_error\"]\n",
+        "\n",
+        "    return rmse_value, model.count_params()\n",
+        "\n",
+        "\n",
+        "def print_stats(rmse_list, num_params, model_name):\n",
+        "    # Report metrics.\n",
+        "    num_trials = len(rmse_list)\n",
+        "    avg_rmse = np.mean(rmse_list)\n",
+        "    std_rmse = np.std(rmse_list)\n",
+        "\n",
+        "    if num_trials == 1:\n",
+        "        print(f\"{model_name}: RMSE = {avg_rmse}; #params = {num_params}\")\n",
+        "    else:\n",
+        "        print(f\"{model_name}: RMSE = {avg_rmse} ± {std_rmse}; #params = {num_params}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tVHJBIJ_62kV"
+      },
+      "source": [
+        "## Real-world example\n",
+        "\n",
+        "Let's use the MovieLens 100K dataset. This dataset is used to train models to\n",
+        "predict users' movie ratings, based on user-related features and movie-related\n",
+        "features.\n",
+        "\n",
+        "### Preparing the dataset\n",
+        "\n",
+        "The dataset processing steps here are similar to what's given in the\n",
+        "[basic ranking](/keras_rs/examples/basic_ranking/)\n",
+        "tutorial. Let's load the dataset, and keep only the useful columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "kipGDLuZ62kW",
+        "outputId": "389e6a82-e5fa-4f31-8bd2-1ee107c9aed8"
+      },
+      "outputs": [],
+      "source": [
+        "ratings_ds = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
+        "ratings_ds = ratings_ds.map(\n",
+        "    lambda x: (\n",
+        "        {\n",
+        "            \"movie_id\": int(x[\"movie_id\"]),\n",
+        "            \"user_id\": int(x[\"user_id\"]),\n",
+        "            \"user_gender\": int(x[\"user_gender\"]),\n",
+        "            \"user_zip_code\": x[\"user_zip_code\"],\n",
+        "            \"user_occupation_text\": x[\"user_occupation_text\"],\n",
+        "            \"bucketized_user_age\": int(x[\"bucketized_user_age\"]),\n",
+        "        },\n",
+        "        x[\"user_rating\"],  # label\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3DrNFuZi62kW"
+      },
+      "source": [
+        "For every feature, let's get the list of unique values, i.e., vocabulary, so\n",
+        "that we can use that for the embedding layer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pczAh0MV62kW"
+      },
+      "outputs": [],
+      "source": [
+        "vocabularies = {}\n",
+        "for feature_name in MOVIELENS_CONFIG[\"int_features\"] + MOVIELENS_CONFIG[\"str_features\"]:\n",
+        "    vocabulary = ratings_ds.batch(10_000).map(lambda x, y: x[feature_name])\n",
+        "    vocabularies[feature_name] = np.unique(np.concatenate(list(vocabulary)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1PLhYqF062kW"
+      },
+      "source": [
+        "One thing we need to do is to use `keras.layers.StringLookup` and\n",
+        "`keras.layers.IntegerLookup` to convert all features into indices, which can\n",
+        "then be fed into embedding layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pLMvMAIF62kW"
+      },
+      "outputs": [],
+      "source": [
+        "lookup_layers = {}\n",
+        "lookup_layers.update(\n",
+        "    {\n",
+        "        feature: keras.layers.IntegerLookup(vocabulary=vocabularies[feature])\n",
+        "        for feature in MOVIELENS_CONFIG[\"int_features\"]\n",
+        "    }\n",
+        ")\n",
+        "lookup_layers.update(\n",
+        "    {\n",
+        "        feature: keras.layers.StringLookup(vocabulary=vocabularies[feature])\n",
+        "        for feature in MOVIELENS_CONFIG[\"str_features\"]\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "ratings_ds = ratings_ds.map(\n",
+        "    lambda x, y: (\n",
+        "        {\n",
+        "            feature_name: lookup_layers[feature_name](x[feature_name])\n",
+        "            for feature_name in vocabularies\n",
+        "        },\n",
+        "        y,\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XBKk-mX962kW"
+      },
+      "source": [
+        "Let's split our data into train and test sets. We also use `cache()` and\n",
+        "`prefetch()` for better performance."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ha3v4rHN62kW"
+      },
+      "outputs": [],
+      "source": [
+        "ratings_ds = ratings_ds.shuffle(100_000)\n",
+        "\n",
+        "train_ds = (\n",
+        "    ratings_ds.take(80_000)\n",
+        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
+        "    .cache()\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")\n",
+        "test_ds = (\n",
+        "    ratings_ds.skip(80_000)\n",
+        "    .batch(MOVIELENS_CONFIG[\"batch_size\"])\n",
+        "    .take(20_000)\n",
+        "    .cache()\n",
+        "    .prefetch(tf.data.AUTOTUNE)\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RXuXvsl062kW"
+      },
+      "source": [
+        "### Building the model\n",
+        "\n",
+        "The model will have embedding layers, followed by DotInteraction and feedforward\n",
+        "layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ww3ZJ6R062kW"
+      },
+      "outputs": [],
+      "source": [
+        "class DLRM(keras.Model):\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        dense_num_units_lst,\n",
+        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        "        **kwargs,\n",
+        "    ):\n",
+        "        super().__init__(**kwargs)\n",
+        "\n",
+        "        # Layers.\n",
+        "\n",
+        "        self.embedding_layers = []\n",
+        "        for feature_name, vocabulary in vocabularies.items():\n",
+        "            self.embedding_layers.append(\n",
+        "                keras.layers.Embedding(\n",
+        "                    input_dim=len(vocabulary) + 1,\n",
+        "                    output_dim=embedding_dim,\n",
+        "                )\n",
+        "            )\n",
+        "\n",
+        "        self.dot_layer = keras_rs.layers.DotInteraction()\n",
+        "\n",
+        "        self.dense_layers = []\n",
+        "        for num_units in dense_num_units_lst:\n",
+        "            self.dense_layers.append(keras.layers.Dense(num_units, activation=\"relu\"))\n",
+        "\n",
+        "        self.output_layer = keras.layers.Dense(1)\n",
+        "\n",
+        "        # Attributes.\n",
+        "        self.dense_num_units_lst = dense_num_units_lst\n",
+        "        self.embedding_dim = embedding_dim\n",
+        "\n",
+        "    def call(self, inputs):\n",
+        "        embeddings = []\n",
+        "        for feature_name, embedding_layer in zip(vocabularies, self.embedding_layers):\n",
+        "            embeddings.append(embedding_layer(inputs[feature_name]))\n",
+        "\n",
+        "        # Pass the list of embeddings to the DotInteraction layer\n",
+        "        x = self.dot_layer(embeddings)\n",
+        "\n",
+        "        for dense_layer in self.dense_layers:\n",
+        "            x = dense_layer(x)\n",
+        "\n",
+        "        x = self.output_layer(x)\n",
+        "\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "OAoTyyzi_Nau",
+        "outputId": "2f469cae-09e1-45fd-853d-79dffc1d5251"
+      },
+      "outputs": [],
+      "source": [
+        "dot_network = DLRM(\n",
+        "    dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
+        "    embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        ")\n",
+        "rmse, dot_network_num_params = train_and_evaluate(\n",
+        "    learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
+        "    epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
+        "    train_data=train_ds,\n",
+        "    test_data=test_ds,\n",
+        "    model= dot_network,\n",
+        "    plot_metrics=True\n",
+        ")\n",
+        "print_stats(\n",
+        "    rmse_list=[rmse],\n",
+        "    num_params=dot_network_num_params,\n",
+        "    model_name=\"Dot Network\",\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E68GrcDN62kW"
+      },
+      "source": [
+        "### Visualizing feature interactions\n",
+        "\n",
+        "The DotInteraction layer itself doesn't have a conventional \"weight\" matrix like a Dense layer. Instead, its function is to compute the dot product between the embedding vectors of your features.\n",
+        "\n",
+        "To visualize the strength of these interactions, we can calculate a matrix representing the pairwise interaction strength between all feature embeddings. A common way to do this is to take the dot product of the embedding matrices for each pair of features and then aggregate the result into a single value (like the mean of the absolute values) that represents the overall interaction strength."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R8WPVVn-Jq81"
+      },
+      "outputs": [],
+      "source": [
+        "def get_dot_interaction_matrix(model, feature_names):\n",
+        "    # Extract the trained embedding weights from the model\n",
+        "    embedding_weights = [layer.get_weights()[0] for layer in model.embedding_layers]\n",
+        "\n",
+        "    num_features = len(feature_names)\n",
+        "    interaction_matrix = np.zeros((num_features, num_features))\n",
+        "\n",
+        "    # Iterate through each pair of features to calculate their interaction strength\n",
+        "    for i in range(num_features):\n",
+        "        for j in range(num_features):\n",
+        "            # Calculate the dot product between the two embedding matrices\n",
+        "            interaction = np.dot(embedding_weights[i], embedding_weights[j].T)\n",
+        "            # Take the mean of the absolute values as a measure of interaction strength\n",
+        "            interaction_strength = np.mean(np.abs(interaction))\n",
+        "            interaction_matrix[i, j] = interaction_strength\n",
+        "\n",
+        "    return interaction_matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 512
+        },
+        "id": "NH-VtGrMJtAt",
+        "outputId": "63eac5d9-8e3e-4b40-bc29-1d51d9827a8d"
+      },
+      "outputs": [],
+      "source": [
+        "# Get the list of feature names in the correct order\n",
+        "feature_names = list(vocabularies.keys())\n",
+        "\n",
+        "# Calculate the interaction matrix\n",
+        "interaction_matrix = get_dot_interaction_matrix(dot_network, feature_names)\n",
+        "\n",
+        "# Visualize the matrix as a heatmap\n",
+        "print(\"\\nVisualizing the feature interaction strengths:\")\n",
+        "visualize_layer(interaction_matrix, feature_names)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IbpjtvnQ62kW"
+      },
+      "outputs": [],
+      "source": [
+        "dlrm_rmse_list = []\n",
+        "\n",
+        "for _ in range(20):\n",
+        "\n",
+        "    dot_network = DLRM(\n",
+        "        dense_num_units_lst=MOVIELENS_CONFIG[\"deep_net_num_units\"],\n",
+        "        embedding_dim=MOVIELENS_CONFIG[\"embedding_dim\"],\n",
+        "    )\n",
+        "    rmse, dot_network_num_params = train_and_evaluate(\n",
+        "        learning_rate=MOVIELENS_CONFIG[\"learning_rate\"],\n",
+        "        epochs=MOVIELENS_CONFIG[\"num_epochs\"],\n",
+        "        train_data=train_ds,\n",
+        "        test_data=test_ds,\n",
+        "        model= dot_network,\n",
+        "    )\n",
+        "    dlrm_rmse_list.append(rmse)\n",
+        "\n",
+        "print_stats(\n",
+        "    rmse_list=dlrm_rmse_list,\n",
+        "    num_params=dot_network_num_params,\n",
+        "    model_name=\"Dot Network\",\n",
+        ")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8mskfVqa-uGx"
+      },
+      "outputs": [],
+      "source": [
+        "def plot_rmse(rmse_list, model_name):\n",
+        "  plt.figure()\n",
+        "  plt.plot(rmse_list)\n",
+        "  plt.title(f'RMSE over trials for {model_name}')\n",
+        "  plt.xlabel('Trial')\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.show()\n",
+        "\n",
+        "plot_rmse(dlrm_rmse_list, \"Dot Network\")"
+      ]
+    }
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