code: def adjust_tensors_for_parallel(hidden_states, *tensors):
      """
      Replicates a given list of tensors based on the shape of the reference tensor (first argument).
      """
      outputs = []
      for tensor in tensors:
          if (
              tensor is not None
              and hidden_states.shape[0] >= tensor.shape[0]
          ):
              repeats = [1] * len(tensor.shape)
              repeats[0] = hidden_states.shape[0] // tensor.shape[0]
              new_tensor = tensor.repeat(*repeats)
              outputs.append(new_tensor)
          else:
              outputs.append(tensor)
      return tuple(outputs)
  
imports: import itertools
  from collections.abc import Sequence
  from typing import List, Optional, Set, Tuple, Union
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: adjust_tensors_for_parallel(hidden_states) and returns: tuple