code: def _input_data_collator(
      batch: Dict[str, Tensor],
      device: Optional[Union[str, torch.device]] = None,
  ) -> Dict[str, Tensor]:
      """Trim model inputs.
  
      This function trims the model inputs to the longest sequence within the batch and put the input on the proper
      device.
  
      """
      max_len = int(batch["attention_mask"].sum(1).max().item())
      input_ids = batch["input_ids"][:, :max_len].to(device)
      attention_mask = batch["attention_mask"][:, :max_len].to(device)
      batch.update(
          {"input_ids": input_ids, "attention_mask": attention_mask}
      )
      return batch
  
imports: import math
  import os
  from collections import Counter, defaultdict
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Union
  import torch
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset
  from torchmetrics.utilities.data import _cumsum
  from torchmetrics.utilities.imports import _TQDM_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
label_desc: Use image node_id 2 for calling a global function with example usage: _input_data_collator(batch, device) and returns: batch