code: def _output_data_collator(
      model_output: Tensor, attention_mask: Tensor, target_len: int
  ) -> Tuple[Tensor, Tensor]:
      """Pad the model output and attention mask to the target length."""
      zeros_shape = list(model_output.shape)
      zeros_shape[2] = target_len - zeros_shape[2]
      model_output = torch.cat(
          [
              model_output,
              torch.zeros(zeros_shape, dtype=model_output.dtype).to(
                  model_output.device
              ),
          ],
          dim=2,
      )
      zeros = torch.zeros(
          zeros_shape[0], zeros_shape[2], dtype=attention_mask.dtype
      ).to(attention_mask.device)
      attention_mask = torch.cat([attention_mask, zeros], dim=1)
      return model_output, attention_mask
  
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 3 for calling a global function with example usage: _output_data_collator(model_output, attention_mask, target_len) and returns: model_output,  attention_mask