class_docstr: Transformer encoder.
class_docstr_tok: ["Transformer","encoder","."]
code: def upgrade_state_dict_named(self, state_dict, name):
      if isinstance(
          self.transformer_embedding.embed_positions,
          SinusoidalPositionalEmbedding,
      ):
          if (
              f"{name}.transformer_embedding.embed_positions.weights"
              in state_dict
          ):
              del state_dict[
                  f"{name}.transformer_embedding.embed_positions.weights"
              ]
          state_dict[
              f"{name}.transformer_embedding.embed_positions._float_tensor"
          ] = torch.FloatTensor(1)
      self.transformer_encoder_given_embeddings.upgrade_state_dict_named(
          state_dict, f"{name}.transformer_encoder_given_embeddings"
      )
      return state_dict
  
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 5 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.upgrade_state_dict_named(state_dict, name) and returns: state_dict