class_docstr: 1D convolution module for custom encoder.
  
  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 use batch normalization after convolution.
      relu: Whether to use a ReLU activation after convolution.
      dropout_rate: Dropout rate.
docstr: Create new mask.
  
  Args:
      mask: Mask of input sequences. (B, 1, T)
  
  Returns:
      mask: Mask of output sequences. (B, 1, sub(T))
code: def create_new_mask(self, mask: torch.Tensor) -> torch.Tensor:
      """Create new mask.
  
      Args:
          mask: Mask of input sequences. (B, 1, T)
  
      Returns:
          mask: Mask of output sequences. (B, 1, sub(T))
  
      """
      if mask is None:
          return mask
  
      if self.padding != 0:
          mask = mask[:, :, : -self.padding]
  
      mask = mask[:, :, :: self.stride]
  
      return 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 3 for calling the Conv1d obj's underlying member method code with example usage: obj.create_new_mask(mask) and returns: mask, mask