code: def add_adapter(self, adapter_type, adapter_dim):
      def _make_identity(linear, eps=1e-5):
          assert isinstance(linear, nn.Linear)
          linear.weight.data.mul_(eps)
          linear.weight.data.fill_diagonal_(1.0)
          if linear.bias is not None:
              linear.bias.data.mul_(eps)
  
      adapter = None
      if adapter_type == "Linear":
          assert adapter_dim > 0
          adapter = nn.Sequential(
              nn.Linear(adapter_dim, adapter_dim),
              LayerNorm(adapter_dim),
          )
          # initialize the adapter as identity matrix first
          _make_identity(adapter[0])
  
      elif adapter_type == "MLP":
          assert adapter_dim > 0
          # assume the model is pre-norm model
          adapter = nn.Sequential(
              nn.Linear(adapter_dim, 2 * adapter_dim),
              nn.ReLU(),
              nn.Linear(2 * adapter_dim, adapter_dim),
              LayerNorm(adapter_dim),
          )
          _make_identity(adapter[0])
          _make_identity(adapter[2])
      return adapter
  
imports: import logging
  from collections import namedtuple
  import torch
  import torch.nn
  from fairseq import checkpoint_utils
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.models.speech_to_text import TransformerDecoder, S2TTransformerEncoder
  from fairseq.models.transformer import TransformerEncoder
  from fairseq.modules import TransformerEncoderLayer, GradMultiply, LayerNorm
label_desc: Use image node_id 2 for calling the SpeechEoSEncoder obj's underlying member method code with example usage: obj.add_adapter(adapter_type, adapter_dim) and returns: adapter