code: def emformer_rnnt_base(num_symbols: int) -> RNNT:
      r"""Builds basic version of Emformer-based :class:`~torchaudio.models.RNNT`.
  
      Args:
          num_symbols (int): The size of target token lexicon.
  
      Returns:
          RNNT:
              Emformer RNN-T model.
      """
      return emformer_rnnt_model(
          input_dim=80,
          encoding_dim=1024,
          num_symbols=num_symbols,
          segment_length=16,
          right_context_length=4,
          time_reduction_input_dim=128,
          time_reduction_stride=4,
          transformer_num_heads=8,
          transformer_ffn_dim=2048,
          transformer_num_layers=20,
          transformer_dropout=0.1,
          transformer_activation="gelu",
          transformer_left_context_length=30,
          transformer_max_memory_size=0,
          transformer_weight_init_scale_strategy="depthwise",
          transformer_tanh_on_mem=True,
          symbol_embedding_dim=512,
          num_lstm_layers=3,
          lstm_layer_norm=True,
          lstm_layer_norm_epsilon=1e-3,
          lstm_dropout=0.3,
      )
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models import Emformer
label_desc: Use image node_id 2 for calling a global function with example usage: emformer_rnnt_base(num_symbols) and returns: emformer_rnnt_model