code: def reset_parameters(self):
      for ii in range(self.adapter_num):
          nn.init.kaiming_uniform_(self.W_a[ii], a=math.sqrt(5))
          nn.init.kaiming_uniform_(self.W_b[ii], a=math.sqrt(5))
          fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
              self.W_a[ii]
          )
          bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
          nn.init.uniform_(self.b_a[ii], -bound, bound)
          fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
              self.W_b[ii]
          )
          bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
          nn.init.uniform_(self.b_b[ii], -bound, bound)
  
      nn.init.ones_(self.ln_W)
      nn.init.zeros_(self.ln_b)
  
imports: import math
  from dataclasses import dataclass, field
  from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.distributed.fully_sharded_data_parallel import FullyShardedDataParallel
  from fairseq.modules import Fp32GroupNorm, Fp32LayerNorm, GradMultiply, GumbelVectorQuantizer, LayerNorm, MultiheadAttention, RelPositionalEncoding, SamePad, TransposeLast
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
  from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import buffered_arange, index_put, is_xla_tensor
  from .utils import pad_to_multiple
label_desc: Use image node_id 2 for calling the AdapterFast obj's underlying member method code with example usage: obj.reset_parameters() without return types