code: def __init__(self, in_dim, hidden_dim, kernel_size, dropout):
      super().__init__()
      self.ffn = nn.Sequential(
          nn.Conv1d(
              in_dim,
              hidden_dim,
              kernel_size=kernel_size,
              padding=(kernel_size - 1) // 2,
          ),
          nn.ReLU(),
          nn.Conv1d(
              hidden_dim,
              in_dim,
              kernel_size=kernel_size,
              padding=(kernel_size - 1) // 2,
          ),
      )
      self.layer_norm = LayerNorm(in_dim)
      self.dropout = self.dropout_module = FairseqDropout(
          p=dropout, module_name=self.__class__.__name__
      )
  
imports: import logging
  import torch
  from torch import nn
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
  from fairseq.models.text_to_speech.tacotron2 import Postnet
  from fairseq.modules import FairseqDropout, LayerNorm, MultiheadAttention, PositionalEmbedding
label_desc: Use image node_id 1 to create a new PositionwiseFeedForward object from inherited base classes: nn with example: obj = PositionwiseFeedForward(in_dim, hidden_dim, kernel_size, dropout)