code: def __init__(
      self,
      embed_dim,
      n_heads,
      hidden_dim,
      kernel_size,
      dropout,
      attention_dropout,
  ):
      super().__init__()
      self.self_attn = MultiheadAttention(
          embed_dim,
          n_heads,
          dropout=attention_dropout,
          self_attention=True,
      )
      self.layer_norm = LayerNorm(embed_dim)
      self.ffn = PositionwiseFeedForward(
          embed_dim, hidden_dim, kernel_size, dropout=dropout
      )
  
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 FFTLayer object from inherited base classes: torch.nn with example: obj = FFTLayer(embed_dim, n_heads, hidden_dim, kernel_size, dropout, attention_dropout)