class_docstr: Feed Forward Inner layers for Transformer.
  
  Args:
      in_channels (int): input tensor channels.
      out_channels (int): output tensor channels.
      hidden_channels (int): inner layers hidden channels.
      kernel_size (int): conv1d filter kernel size.
      dropout_p (float, optional): dropout rate. Defaults to 0.
class_docstr_tok: ["Feed","Forward","Inner","layers","for","Transformer",".","Args",":","in_channels","(","int",")",":","input","tensor","channels",".","out_channels","(","int",")",":","output","tensor","channels",".","hidden_channels","(","int",")",":","inner","layers","hidden","channels",".","kernel_size","(","int",")",":","conv1d","filter","kernel","size",".","dropout_p","(","float",",","optional",")",":","dropout","rate",".","Defaults","to","0","."]
code: def forward(self, x, x_mask):
      x = self.conv_1(x * x_mask)
      x = torch.relu(x)
      x = self.dropout(x)
      x = self.conv_2(x * x_mask)
      return x * x_mask
  
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.glow_tts.glow import LayerNorm
label_desc: Use image node_id 2 for calling the FeedForwardNetwork obj's underlying member method code with example usage: obj.forward(x, x_mask) and returns: unknown