class_docstr: Conformer layer that constitutes Conformer.
  
  Args:
      input_dim (int): input dimension.
      ffn_dim (int): hidden layer dimension of feedforward network.
      num_attention_heads (int): number of attention heads.
      depthwise_conv_kernel_size (int): kernel size of depthwise convolution layer.
      dropout (float, optional): dropout probability. (Default: 0.0)
      use_group_norm (bool, optional): use ``GroupNorm`` rather than ``BatchNorm1d``
          in the convolution module. (Default: ``False``)
      convolution_first (bool, optional): apply the convolution module ahead of
          the attention module. (Default: ``False``)
class_docstr_tok: ["Conformer","layer","that","constitutes","Conformer",".","Args",":","input_dim","(","int",")",":","input","dimension",".","ffn_dim","(","int",")",":","hidden","layer","dimension","of","feedforward","network",".","num_attention_heads","(","int",")",":","number","of","attention","heads",".","depthwise_conv_kernel_size","(","int",")",":","kernel","size","of","depthwise","convolution","layer",".","dropout","(","float",",","optional",")",":","dropout","probability",".","(","Default",":","0.0",")","use_group_norm","(","bool",",","optional",")",":","use","``","GroupNorm","``","rather","than","``","BatchNorm1d","``","in","the","convolution","module",".","(","Default",":","``","False","``",")","convolution_first","(","bool",",","optional",")",":","apply","the","convolution","module","ahead","of","the","attention","module",".","(","Default",":","``","False","``",")"]
code: def _apply_convolution(self, input: torch.Tensor) -> torch.Tensor:
      residual = input
      input = input.transpose(0, 1)
      input = self.conv_module(input)
      input = input.transpose(0, 1)
      input = residual + input
      return input
  
imports: from typing import Optional, Tuple
  import torch
label_desc: Use image node_id 2 for calling the ConformerLayer obj's underlying member method code with example usage: obj._apply_convolution(input) and returns: input