code: def forward(self, x, padding_mask=None):
      # B x T x C
      residual = x
      x = x.transpose(0, 1)
      x, _ = self.self_attn(
          query=x,
          key=x,
          value=x,
          key_padding_mask=padding_mask,
          need_weights=False,
      )
      x = x.transpose(0, 1)
      x = self.layer_norm(x + residual)
      return self.ffn(x)
  
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 2 for calling the FFTLayer obj's underlying member method code with example usage: obj.forward(x, padding_mask) and returns: self