code: def _get_conv1d_layer(
      in_channels: int,
      out_channels: int,
      kernel_size: int = 1,
      stride: int = 1,
      padding: Optional[Union[str, int, Tuple[int]]] = None,
      dilation: int = 1,
      bias: bool = True,
      w_init_gain: str = "linear",
  ) -> torch.nn.Conv1d:
      r"""1D convolution with xavier uniform initialization.
  
      Args:
          in_channels (int): Number of channels in the input image.
          out_channels (int): Number of channels produced by the convolution.
          kernel_size (int, optional): Number of channels in the input image. (Default: ``1``)
          stride (int, optional): Number of channels in the input image. (Default: ``1``)
          padding (str, int or tuple, optional): Padding added to both sides of the input.
              (Default: dilation * (kernel_size - 1) / 2)
          dilation (int, optional): Number of channels in the input image. (Default: ``1``)
          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.Conv1d): The corresponding Conv1D layer.
      """
      if padding is None:
          if kernel_size % 2 != 1:
              raise ValueError("kernel_size must be odd")
          padding = int(dilation * (kernel_size - 1) / 2)
  
      conv1d = torch.nn.Conv1d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          bias=bias,
      )
  
      torch.nn.init.xavier_uniform_(
          conv1d.weight, gain=torch.nn.init.calculate_gain(w_init_gain)
      )
  
      return conv1d
  
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 2 for calling a global function with example usage: _get_conv1d_layer(in_channels, out_channels, kernel_size, stride, padding, dilation, bias, w_init_gain) and returns: conv1d