class_docstr: 1D causal convolution module for custom decoder.
  
  Args:
      idim: Input dimension.
      odim: Output dimension.
      kernel_size: Size of the convolving kernel.
      stride: Stride of the convolution.
      dilation: Spacing between the kernel points.
      groups: Number of blocked connections from input channels to output channels.
      bias: Whether to add a learnable bias to the output.
      batch_norm: Whether to apply batch normalization.
      relu: Whether to pass final output through ReLU activation.
      dropout_rate: Dropout rate.
docstr: Forward CausalConv1d for custom decoder.
  
  Args:
      sequence: CausalConv1d input sequences. (B, U, D_in)
      mask: Mask of CausalConv1d input sequences. (B, 1, U)
  
  
  Returns:
      sequence: CausalConv1d output sequences. (B, sub(U), D_out)
      mask: Mask of CausalConv1d output sequences. (B, 1, sub(U))
code: def forward(
      self,
      sequence: torch.Tensor,
      mask: torch.Tensor,
      cache: Optional[torch.Tensor] = None,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Forward CausalConv1d for custom decoder.
  
      Args:
          sequence: CausalConv1d input sequences. (B, U, D_in)
          mask: Mask of CausalConv1d input sequences. (B, 1, U)
  
  
      Returns:
          sequence: CausalConv1d output sequences. (B, sub(U), D_out)
          mask: Mask of CausalConv1d output sequences. (B, 1, sub(U))
  
      """
      sequence = sequence.transpose(1, 2)
      sequence = self.causal_conv1d(sequence)
  
      if self.padding != 0:
          sequence = sequence[:, :, : -self.padding]
  
      if self.batch_norm:
          sequence = self.bn(sequence)
  
      sequence = self.dropout(sequence)
  
      if self.relu:
          sequence = self.relu_func(sequence)
  
      sequence = sequence.transpose(1, 2)
  
      return sequence, mask
  
file_path: espnet/espnet/nets/pytorch_backend/transducer/conv1d_nets.py
imports: from typing import Optional, Tuple, Union
  import torch
label_desc: Use image node_id 2 for calling the CausalConv1d obj's underlying member method code with example usage: obj.forward(sequence, mask, cache) and returns: sequence,  mask