code: def __init__(self, args):
      super().__init__(args)
      self.args = args
      self.dropout = args.dropout
      self.embedding_dim = args.encoder_embed_dim
      self.pos_enc_type = args.pos_enc_type
      max_source_positions = self.max_positions()
  
      if self.pos_enc_type == "rel_pos":
          self.embed_positions = RelPositionalEncoding(
              max_source_positions, self.embedding_dim
          )
      elif self.pos_enc_type == "rope":
          self.embed_positions = None
      else:
          raise Exception("Unsupported positional encoding type")
  
      self.layers = nn.ModuleList(
          [
              self.build_encoder_layer(args)
              for _ in range(args.encoder_layers)
          ]
      )
      self.layer_norm_first = args.layer_norm_first
      self.layer_norm = LayerNorm(self.embedding_dim)
      self.layerdrop = args.encoder_layerdrop
  
      self.apply(init_bert_params)
  
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 to create a new ConformerEncoder object from inherited base classes: TransformerEncoder with example: obj = ConformerEncoder(args)