code: def __call__(self, data):
      batch = {}
      input_ids = pad_sequence(
          [default_collate(f["input_ids"]) for f in data],
          padding_value=self.pad_token_id,
          batch_first=True,
      )
  
      labels = pad_sequence(
          [default_collate(f["labels"]) for f in data],
          padding_value=DST.DEFAULT_LABEL_PADDING_NUM,
          batch_first=True,
      )
      attention_mask = pad_sequence(
          [default_collate(f["attention_mask"]) for f in data],
          padding_value=0,
          batch_first=True,
      )
      image = torch.concat(
          [default_collate(f["image"]) for f in data], dim=0
      ).reshape((-1,) + data[0]["image"][0].shape[-3:])
      image_num = [f["image_num"] for f in data]
      batch["input_ids"] = input_ids
      batch["labels"] = labels
      batch["attention_mask"] = attention_mask
      batch["image"] = image
      batch["image_num"] = image_num
      return batch
  
file_path: deepspeedexamples/applications/DeepSpeed-VisualChat/utils/data/utils.py
imports: import torch
  from torch.utils.data import Subset
  from torch.nn.utils.rnn import pad_sequence
  import numpy
  import shutil
  from torch.utils.data.dataloader import default_collate
  import utils.data.DST
label_desc: Use image node_id 2 for calling the DataCollatorPadToMaxLen obj's underlying member method code with example usage: obj.__call__(data) and returns: batch