Question during the inference

[gujiaqivadin]

(vlm) (base) root@workspace-gujiaqi-workspace-gujiaqi-6d24da8c-7c55bd5d67-5rcdd:/mnt/gujiaqi-pvc/mypvc/workspace/llm/tq-chart-vlm# CUDA_VISIBLE_DEVICES=0 python models/chartgemma.py
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 1.76it/s]
Traceback (most recent call last):
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 183, in convert_to_tensors
tensor = as_tensor(value)
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 142, in as_tensor
return torch.tensor(value)
RuntimeError: Could not infer dtype of numpy.float32

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/mnt/gujiaqi-pvc/mypvc/workspace/llm/tq-chart-vlm/models/chartgemma.py", line 29, in
inputs = processor(text=input_text, images=image, return_tensors="pt")
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/models/paligemma/processing_paligemma.py", line 254, in call
pixel_values = self.image_processor(
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/image_processing_utils.py", line 41, in call
return self.preprocess(images, **kwargs)
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/utils/generic.py", line 852, in wrapper
return func(*args, **valid_kwargs)
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/models/siglip/image_processing_siglip.py", line 241, in preprocess
return BatchFeature(data=data, tensor_type=return_tensors)
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 79, in init
self.convert_to_tensors(tensor_type=tensor_type)
File "/mnt/gujiaqi-pvc/mypvc/workspace/anaconda3/envs/vlm/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 189, in convert_to_tensors
raise ValueError(
ValueError: Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.

I got wrong output duriing running the inference demo, can you provide your transformer's verision or other setting.