class_docstr: Highway Network module.
  
  This is a module of Highway Network introduced in `Highway Networks`_.
  
  .. _`Highway Networks`: https://arxiv.org/abs/1505.00387
class_docstr_tok: ["Highway","Network","module",".","This","is","a","module","of","Highway","Network","introduced","in","`","Highway","Networks","`","_",".","..","_","`","Highway","Networks","`",":","https",":","\/\/arxiv.org\/abs\/1505.00387"]
docstr: Calculate forward propagation.
  
  Args:
      x (Tensor): Batch of inputs (B, ..., idim).
  
  Returns:
      Tensor: Batch of outputs, which are the same shape as inputs (B, ..., idim).
docstr_tok: ["Calculate","forward","propagation",".","Args",":","x","(","Tensor",")",":","Batch","of","inputs","(","B",",","...",",","idim",")",".","Returns",":","Tensor",":","Batch","of","outputs",",","which","are","the","same","shape","as","inputs","(","B",",","...",",","idim",")","."]
code: def forward(self, x):
      """Calculate forward propagation.
  
      Args:
          x (Tensor): Batch of inputs (B, ..., idim).
  
      Returns:
          Tensor: Batch of outputs, which are the same shape as inputs (B, ..., idim).
  
      """
      proj = self.projection(x)
      gate = self.gate(x)
      return proj * gate + x * (1.0 - gate)
  
file_path: espnet/espnet/nets/pytorch_backend/tacotron2/cbhg.py
imports: import torch
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
label_desc: Use image node_id 2 for calling the HighwayNet obj's underlying member method code with example usage: obj.forward(x) and returns: unknown