class_docstr: Implements a Linformer Encoder Layer used in BERT/XLM style pre-trained
  models.
code: def build_self_attention(self, embed_dim, args):
      return MultiheadLinearAttention(
          embed_dim,
          args.encoder_attention_heads,
          dropout=args.dropout,
          self_attention=True,
          q_noise=args.quant_noise_pq,
          qn_block_size=args.quant_noise_pq_block_size,
          compressed=args.compressed,
          max_seq_len=args.max_positions,
          shared_kv_compressed=args.shared_kv_compressed,
          shared_compress_layer=self.shared_compress_layer[0],
          freeze_compress=args.freeze_compress,
      )
  
file_path: fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py
imports: import torch
  from fairseq import utils
  from fairseq.modules import TransformerEncoderLayer
  from .multihead_linear_attention import MultiheadLinearAttention
label_desc: Use image node_id 2 for calling the LinformerTransformerEncoderLayer obj's underlying member method code with example usage: obj.build_self_attention(embed_dim, args) and returns: MultiheadLinearAttention