code: def __init__(self, args):
      super().__init__(args)
      stride = self.conv_layer_stride(args)
      trf_left_context = args.segment_left_context // stride
      trf_right_context = args.segment_right_context // stride
      context_config = [trf_left_context, trf_right_context]
      self.transformer_layers = nn.ModuleList(
          [
              NoSegAugmentedMemoryTransformerEncoderLayer(
                  input_dim=args.encoder_embed_dim,
                  num_heads=args.encoder_attention_heads,
                  ffn_dim=args.encoder_ffn_embed_dim,
                  num_layers=args.encoder_layers,
                  dropout_in_attn=args.dropout,
                  dropout_on_attn=args.dropout,
                  dropout_on_fc1=args.dropout,
                  dropout_on_fc2=args.dropout,
                  activation_fn=args.activation_fn,
                  context_config=context_config,
                  segment_size=args.segment_length,
                  max_memory_size=args.max_memory_size,
                  scaled_init=True,  # TODO: use constant for now.
                  tanh_on_mem=args.amtrf_tanh_on_mem,
              )
          ]
      )
      self.conv_transformer_encoder = ConvTransformerEncoder(args)
  
imports: from fairseq import checkpoint_utils
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.speech_to_text import ConvTransformerModel, convtransformer_espnet, ConvTransformerEncoder
  from fairseq.models.speech_to_text.modules.augmented_memory_attention import augmented_memory, SequenceEncoder, AugmentedMemoryConvTransformerEncoder
  from torch import nn, Tensor
  from typing import Dict, List
  from fairseq.models.speech_to_text.modules.emformer import NoSegAugmentedMemoryTransformerEncoderLayer
label_desc: Use image node_id 1 to create a new ConvTransformerEmformerEncoder object from inherited base classes: ConvTransformerEncoder with example: obj = ConvTransformerEmformerEncoder(args)