code: def forward(
      self, x, padding_mask=None, streaming_mask=None, layer=None
  ):
      x, layer_results = self.extract_features(
          x, padding_mask, streaming_mask, layer
      )
  
      if self.layer_norm_first and layer is None:
          x = self.layer_norm(x)
  
      return x, layer_results
  
imports: import logging
  import math
  from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import LayerNorm
  from TTS.vc.modules.freevc.wavlm.modules import Fp32GroupNorm, Fp32LayerNorm, GLU_Linear, GradMultiply, MultiheadAttention, SamePad, TransposeLast, get_activation_fn, init_bert_params
label_desc: Use image node_id 2 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.forward(x, padding_mask, streaming_mask, layer) and returns: x,  layer_results