code: def ds_transforms(example_batch):
      image_size = processor.image_processor.image_size
      image_mean = processor.image_processor.image_mean
      image_std = processor.image_processor.image_std
      image_transform = transforms.Compose(
          [
              convert_to_rgb,
              transforms.RandomResizedCrop(
                  (image_size, image_size),
                  scale=(0.9, 1.0),
                  interpolation=transforms.InterpolationMode.BICUBIC,
              ),
              transforms.ToTensor(),
              transforms.Normalize(mean=image_mean, std=image_std),
          ]
      )
      prompts = []
      for i in range(len(example_batch["caption"])):
          # We split the captions to avoid having very long examples, which would require more GPU ram during training
          caption = example_batch["caption"][i].split(".")[0]
          try:
              # There are a handful of images that are not hosted anymore. This is a small (dummy) hack to skip these
              processor.image_processor.fetch_images(
                  example_batch["image_url"][i]
              )
          except Exception:
              print(
                  "Warning: at least one image couldn't be retrieved from the internet in an example. Skipping the"
                  " batch."
              )
          prompts.append(
              [
                  example_batch["image_url"][i],
                  f"Question: What's on the picture? Answer: This is {example_batch['name'][i]}. {caption}</s>",
              ],
          )
      inputs = processor(
          prompts, transform=image_transform, return_tensors="pt"
      ).to(device)
      inputs["labels"] = inputs["input_ids"]
      return inputs
  
imports: import torch
  import torchvision.transforms
  from datasets import load_dataset
  from PIL import Image
  from transformers import AutoProcessor, IdeficsForVisionText2Text, Trainer, TrainingArguments
label_desc: Use image node_id 2 for calling a global function with example usage: ds_transforms(example_batch) and returns: inputs