class_docstr: Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
  models.
code: def build_self_attention(
      self,
      embed_dim,
      num_attention_heads,
      dropout,
      self_attention,
      q_noise,
      qn_block_size,
  ):
      return MultiheadAttention(
          embed_dim,
          num_attention_heads,
          dropout=dropout,
          self_attention=True,
          q_noise=q_noise,
          qn_block_size=qn_block_size,
      )
  
file_path: fairseq/fairseq/modules/transformer_sentence_encoder_layer.py
imports: from typing import Callable, Optional
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.modules import LayerNorm, MultiheadAttention
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from fairseq.modules.quant_noise import quant_noise
label_desc: Use image node_id 4 for calling the TransformerSentenceEncoderLayer obj's underlying member method code with example usage: obj.build_self_attention(embed_dim, num_attention_heads, dropout, _attention, q_noise, qn_block_size) and returns: MultiheadAttention