class_docstr: Transformer encoder.
class_docstr_tok: ["Transformer","encoder","."]
docstr: This allows adversarial trainer to turn on and off embedding noising
  layers. In regular training, this mode is off, and it is not included
  in forward pass.
docstr_tok: ["This","allows","adversarial","trainer","to","turn","on","and","off","embedding","noising","layers",".","In","regular","training",",","this","mode","is","off",",","and","it","is","not","included","in","forward","pass","."]
code: def set_embed_noising_mode(self, mode=True):
      """This allows adversarial trainer to turn on and off embedding noising
      layers. In regular training, this mode is off, and it is not included
      in forward pass.
      """
      self.embed_noising_mode = mode
  
imports: import math
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import AdaptiveSoftmax, LayerNorm, MultiheadAttention, SinusoidalPositionalEmbedding
  from pytorch_translate import utils, vocab_reduction
  from pytorch_translate.average_attention import AverageAttention
  from pytorch_translate.common_layers import TransformerEmbedding, TransformerEncoderGivenEmbeddings, TransformerTokenEmbedding, VariableTracker
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 7 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.set_embed_noising_mode(mode) without return types