class_docstr: Implementation of the Transformer Sentence Encoder. This directly makes
  use of the TransformerSentenceEncoder module in Fairseq.
  
  A few interesting config options:
      - encoder_normalize_before detemines whether the layer norm is applied
        before or after self_attention. This is similar to original
        implementation from Google.
      - activation_fn can be set to 'gelu' instead of the default of 'relu'.
code: def _embedding(self):
      # used to tie weights in MaskedLM model
      return self.sentence_encoder.embed_tokens
  
file_path: pytext/pytext/models/representations/transformer_sentence_encoder.py
imports: from typing import Tuple
  import torch
  from fairseq.modules import TransformerSentenceEncoder
  from pytext.config import ConfigBase
  from pytext.models.representations.traced_transformer_encoder import TracedTransformerEncoder
  from pytext.models.representations.transformer_sentence_encoder_base import TransformerSentenceEncoderBase
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 4 for calling the TransformerSentenceEncoder obj's underlying member method code with example usage: obj._embedding() and returns: self