class_docstr: Conformer convolution module.
  
  Args:
      input_dim (int): input dimension.
      num_channels (int): number of depthwise convolution layer input channels.
      depthwise_kernel_size (int): kernel size of depthwise convolution layer.
      dropout (float, optional): dropout probability. (Default: 0.0)
      bias (bool, optional): indicates whether to add bias term to each convolution layer. (Default: ``False``)
      use_group_norm (bool, optional): use GroupNorm rather than BatchNorm. (Default: ``False``)
class_docstr_tok: ["Conformer","convolution","module",".","Args",":","input_dim","(","int",")",":","input","dimension",".","num_channels","(","int",")",":","number","of","depthwise","convolution","layer","input","channels",".","depthwise_kernel_size","(","int",")",":","kernel","size","of","depthwise","convolution","layer",".","dropout","(","float",",","optional",")",":","dropout","probability",".","(","Default",":","0.0",")","bias","(","bool",",","optional",")",":","indicates","whether","to","add","bias","term","to","each","convolution","layer",".","(","Default",":","``","False","``",")","use_group_norm","(","bool",",","optional",")",":","use","GroupNorm","rather","than","BatchNorm",".","(","Default",":","``","False","``",")"]
docstr: Args:
      input (torch.Tensor): with shape `(B, T, D)`.
  
  Returns:
      torch.Tensor: output, with shape `(B, T, D)`.
docstr_tok: ["Args",":","input","(","torch.Tensor",")",":","with","shape","`","(","B",",","T",",","D",")","`",".","Returns",":","torch.Tensor",":","output",",","with","shape","`","(","B",",","T",",","D",")","`","."]
code: def forward(self, input: torch.Tensor) -> torch.Tensor:
      r"""
      Args:
          input (torch.Tensor): with shape `(B, T, D)`.
  
      Returns:
          torch.Tensor: output, with shape `(B, T, D)`.
      """
      x = self.layer_norm(input)
      x = x.transpose(1, 2)
      x = self.sequential(x)
      return x.transpose(1, 2)
  
imports: from typing import Optional, Tuple
  import torch
label_desc: Use image node_id 2 for calling the _ConvolutionModule obj's underlying member method code with example usage: obj.forward(input) and returns: x