code: def create_conv_package(
      index: int,
      activation: Activation,
      in_channels: int,
      out_channels: int,
      kernel_size: int,
      causal: bool,
      dilated: bool,
      separable: bool,
      bottleneck: int,
      weight_norm: bool,
  ):
      """
      Creates a convolutional layer with the specified arguments.
  
      Args:
          index (int): Index of a convolutional layer in the stack.
          activation (Activation): Activation function.
          in_channels (int): Number of input channels.
          out_channels (int): Number of output channels.
          kernel_size (int): Size of 1d convolutional filter.
          causal (bool): Whether the convolution is causal or not. If set, it
          accounts for the temporal ordering of the inputs.
          dilated (bool): Whether the convolution is dilated or not. If set,
          the receptive field of the convolutional stack grows exponentially.
          separable (bool): Whether to use depthwise separable convolutions
          or not -- see `SeparableConv1d`.
          bottleneck (int): Bottleneck channel dimension for depthwise separable
          convolutions. See `SeparableConv1d` for an in-depth explanation.
          weight_norm (bool): Whether to add weight normalization to the
          regular convolutions or not.
  
      """
  
      if not separable and bottleneck > 0:
          raise RuntimeError(
              "Bottleneck layers can only be used with separable convolutions"
          )
  
      if separable and weight_norm:
          raise RuntimeError(
              "Weight normalization is not supported for separable convolutions"
          )
  
      def _compute_dilation(index, dilated):
          """
          If set, the dilation factor increases by a factor of two for each
          successive convolution to increase the receptive field exponentially.
  
          """
  
          if dilated:
              return 2**index
          return 1
  
      def _compute_padding(kernel_size, dilation, causal):
          """
          Non-causal convolutions are centered, so they will consume ((k - 1) // 2) * d
          padding on both the left and the right of the sequence. Causal convolutions
          are shifted to the left (to account for temporal ordering), so they will
          only consume padding from the left. Therefore, we pad this side with the
          full amount (k - 1) * d and remove the excess right-padding with `Trim1d`.
  
          """
  
          if causal:
              return (kernel_size - 1) * dilation
          return ((kernel_size - 1) // 2) * dilation
  
      def _compute_out_channels(out_channels, activation):
          """
          Gated Linear Unit (GLU) activations train two groups of convolutions,
          then linearly combine their outputs through a gating mechanism. We
          double the number of `out_channels` to mimic these two groups.
  
          """
  
          if activation == Activation.GLU:
              return out_channels * 2
          return out_channels
  
      package = []
      dilation = _compute_dilation(index, dilated)
      padding = _compute_padding(kernel_size, dilation, causal)
      out_channels = _compute_out_channels(out_channels, activation)
  
      if separable:
          package.append(
              SeparableConv1d(
                  in_channels,
                  out_channels,
                  kernel_size,
                  padding,
                  dilation,
                  bottleneck,
              )
          )
      else:
          conv = nn.Conv1d(
              in_channels,
              out_channels,
              kernel_size,
              padding=padding,
              dilation=dilation,
          )
          if weight_norm:
              conv = nn.utils.weight_norm(conv)
          package.append(conv)
  
      if causal:
          package.append(Trim1d(padding))
  
      return (
          package[0] if len(package) == 1 else nn.Sequential(*package)
      )
  
imports: import math
  import torch
  import torch.nn
  from pytext.config.module_config import Activation, CNNParams, PoolingType
  from pytext.models.representations.representation_base import RepresentationBase
  from pytext.optimizer import get_activation
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 2 for calling a global function with example usage: create_conv_package(index, activation, in_channels, out_channels, kernel_size, causal, dilated, separable, bottleneck, weight_norm) and returns: unknown, int, unknown, out_channels, unknown, unknown, unknown