code: def _get_linear_layer(
      in_dim: int,
      out_dim: int,
      bias: bool = True,
      w_init_gain: str = "linear",
  ) -> torch.nn.Linear:
      r"""Linear layer with xavier uniform initialization.
  
      Args:
          in_dim (int): Size of each input sample.
          out_dim (int): Size of each output sample.
          bias (bool, optional): If set to ``False``, the layer will not learn an additive bias. (Default: ``True``)
          w_init_gain (str, optional): Parameter passed to ``torch.nn.init.calculate_gain``
              for setting the gain parameter of ``xavier_uniform_``. (Default: ``linear``)
  
      Returns:
          (torch.nn.Linear): The corresponding linear layer.
      """
      linear = torch.nn.Linear(in_dim, out_dim, bias=bias)
      torch.nn.init.xavier_uniform_(
          linear.weight, gain=torch.nn.init.calculate_gain(w_init_gain)
      )
      return linear
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
label_desc: Use image node_id 1 for calling a global function with example usage: _get_linear_layer(in_dim, out_dim, bias, w_init_gain) and returns: linear