chunked/nltk_chunking/tasks_video_classification/chunk_66.txt

def collate_fn(examples):
     # permute to (num_frames, num_channels, height, width)
     pixel_values = torch.stack(
         [example["video"].permute(1, 0, 2, 3) for example in examples]
     )
     labels = torch.tensor([example["label"] for example in examples])
     return {"pixel_values": pixel_values, "labels": labels}

Then you just pass all of this along with the datasets to Trainer:
 

trainer = Trainer(
     model,
     args,
     train_dataset=train_dataset,
     eval_dataset=val_dataset,
     tokenizer=image_processor,
     compute_metrics=compute_metrics,
     data_collator=collate_fn,
 )

You might wonder why you passed along the image_processor as a tokenizer when you preprocessed the data already.