class_docstr: Dummy encoder which outputs None. Used for LM training.
class_docstr_tok: ["Dummy","encoder","which","outputs","None",".","Used","for","LM","training","."]
code: def forward(self, src_tokens, src_lengths):
      bsz = src_lengths.size(0)
      ones = maybe_cuda(torch.ones((self.num_layers, bsz, 1)))
      dummy_out = maybe_cuda(torch.ones((1, bsz, 1)))
  
      # ones are returned so that FC layer corresponds to learned initial
      # state for language model
      return dummy_out, ones, ones, src_lengths, src_tokens, dummy_out
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.onnx.operators
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import AdaptiveSoftmax
  from pytorch_translate import attention, rnn_cell, utils, vocab_reduction
  from pytorch_translate.common_layers import ContextEmbedding, DecoderWithOutputProjection, Embedding, Linear, RNNLayer, VariableTracker
  from pytorch_translate.data import data
  from pytorch_translate.multi_model import MultiDecoder, MultiEncoder
  from pytorch_translate.multilingual import MultilingualDecoder, MultilingualEncoder
  from pytorch_translate.ngram import NGramDecoder
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.utils import maybe_cat, maybe_cuda, torch_find
  from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence, pad_packed_sequence
label_desc: Use image node_id 2 for calling the DummyEncoder obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths) and returns: dummy_out,  ones,  ones,  src_lengths,  src_tokens,  dummy_out