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 __init__(
      self,
      in_channels,
      out_channels,
      hidden_channels,
      kernel_size,
      dropout_p=0.0,
  ):
      super().__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.hidden_channels = hidden_channels
      self.kernel_size = kernel_size
      self.dropout_p = dropout_p
  
      self.conv_1 = nn.Conv1d(
          in_channels,
          hidden_channels,
          kernel_size,
          padding=kernel_size // 2,
      )
      self.conv_2 = nn.Conv1d(
          hidden_channels,
          out_channels,
          kernel_size,
          padding=kernel_size // 2,
      )
      self.dropout = nn.Dropout(dropout_p)
  
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 1 to create a new FeedForwardNetwork object from inherited base classes: nn with example: obj = FeedForwardNetwork(in_channels, out_channels, hidden_channels, kernel_size, dropout_p)