class_docstr: Multi-layered conv1d for Transformer block.
  
  This is a module of multi-leyered conv1d designed
  to replace positionwise feed-forward network
  in Transforner block, which is introduced in
  `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
  
  .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
      https://arxiv.org/pdf/1905.09263.pdf
docstr: Calculate forward propagation.
  
  Args:
      x (torch.Tensor): Batch of input tensors (B, T, in_chans).
  
  Returns:
      torch.Tensor: Batch of output tensors (B, T, hidden_chans).
code: def forward(self, x):
      """Calculate forward propagation.
  
      Args:
          x (torch.Tensor): Batch of input tensors (B, T, in_chans).
  
      Returns:
          torch.Tensor: Batch of output tensors (B, T, hidden_chans).
  
      """
      x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
      return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
  
file_path: espnet/espnet/nets/pytorch_backend/transformer/multi_layer_conv.py
imports: import torch
label_desc: Use image node_id 2 for calling the MultiLayeredConv1d obj's underlying member method code with example usage: obj.forward(x) and returns: self