code: def prune_layer(
      layer: Union[nn.Linear, Conv1D],
      index: torch.LongTensor,
      dim: Optional[int] = None,
  ) -> Union[nn.Linear, Conv1D]:
      """
      Prune a Conv1D or linear layer to keep only entries in index.
  
      Used to remove heads.
  
      Args:
          layer (`Union[torch.nn.Linear, Conv1D]`): The layer to prune.
          index (`torch.LongTensor`): The indices to keep in the layer.
          dim (`int`, *optional*): The dimension on which to keep the indices.
  
      Returns:
          `torch.nn.Linear` or [`~pytorch_utils.Conv1D`]: The pruned layer as a new layer with `requires_grad=True`.
      """
      if isinstance(layer, nn.Linear):
          return prune_linear_layer(
              layer, index, dim=0 if dim is None else dim
          )
      elif isinstance(layer, Conv1D):
          return prune_conv1d_layer(
              layer, index, dim=1 if dim is None else dim
          )
      else:
          raise ValueError(
              f"Can't prune layer of class {layer.__class__}"
          )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pytorch_utils.py
imports: import inspect
  from typing import Callable, List, Optional, Set, Tuple, Union
  import torch
  from packaging import version
  from torch import _softmax_backward_data, nn
  from .utils import logging
label_desc: Use image node_id 5 for calling a global function with example usage: prune_layer(layer, index, dim) and returns: prune_linear_layer, prune_conv1d_layer