init question

Author: komaksym

questions/195_gradient-checkpointing/description.md

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+## Problem
+
+Write a concise problem description here.

questions/195_gradient-checkpointing/example.json

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+{
+  "input": "...",
+  "output": "...",
+  "reasoning": "Explain why the output follows from the input."
+}

questions/195_gradient-checkpointing/learn.md

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+## Solution Explanation
+
+Add intuition, math, and step-by-step reasoning here.
+
+### Writing Mathematical Expressions with LaTeX
+
+This editor supports LaTeX for rendering mathematical equations and expressions. Here's how you can use it:
+
+1. **Inline Math**: 
+   - Wrap your expression with single `$` symbols.
+   - Example: `$E = mc^2$` → Renders as: ( $E = mc^2$ )
+
+2. **Block Math**:
+   - Wrap your expression with double `$$` symbols.
+   - Example: 
+     ```
+     $$
+     \int_a^b f(x) \, dx
+     $$
+     ```
+     Renders as:
+     $$
+     \int_a^b f(x) \, dx
+     $$
+
+3. **Math Functions**:
+   - Use standard LaTeX functions like `\frac`, `\sqrt`, `\sum`, etc.
+   - Examples:
+     - `$\frac{a}{b}$` → ( $\frac{a}{b}$ )
+     - `$\sqrt{x}$` → ( $\sqrt{x}$ )
+
+4. **Greek Letters and Symbols**:
+   - Use commands like `\alpha`, `\beta`, etc., for Greek letters.
+   - Example: `$\alpha + \beta = \gamma$` → ( $\alpha + \beta = \gamma$ )
+
+5. **Subscripts and Superscripts**:
+   - Use `_{}` for subscripts and `^{}` for superscripts.
+   - Examples:
+     - `$x_i$` → ( $x_i$ )
+     - `$x^2$` → ( $x^2$ )
+
+6. **Combined Examples**:
+   - `$\sum_{i=1}^n i^2 = \frac{n(n+1)(2n+1)}{6}$`
+     Renders as:
+     $\sum_{i=1}^n i^2 = \frac{n(n+1)(2n+1)}{6}$
+
+Feel free to write your own mathematical expressions, and they will be rendered beautifully in the preview!

questions/195_gradient-checkpointing/meta.json

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+{
+  "id": "XXX",
+  "title": "TITLE GOES HERE",
+  "difficulty": "medium",
+  "category": "Machine Learning",
+  "video": "",
+  "likes": "0",
+  "dislikes": "0",
+  "contributor": [],
+  "tinygrad_difficulty": "",
+  "pytorch_difficulty": ""
+}

questions/195_gradient-checkpointing/pytorch/solution.py

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+def your_function(...):
+    ...

questions/195_gradient-checkpointing/pytorch/starter_code.py

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+def your_function(...):
+    pass

questions/195_gradient-checkpointing/pytorch/tests.json

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+[
+  {
+    "test": "print(your_function(...))",
+    "expected_output": "..."
+  }
+]

questions/195_gradient-checkpointing/solution.py

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+def your_function(...):
+    # reference implementation
+    ...

questions/195_gradient-checkpointing/starter_code.py

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+# Implement your function below.
+
+def your_function(...):
+    pass

questions/195_gradient-checkpointing/tests.json

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+[
+  {
+    "test": "print(your_function(...))",
+    "expected_output": "..."
+  }
+]