code: def __init__(
      self,
      input_features=240,
      vocab_size=29,
      enc_hidden_size=1024,
      pred_hidden_size=320,
      joint_hidden_size=512,
      pre_enc_layers=2,
      post_enc_layers=3,
      pred_layers=2,
      stack_time_factor=2,
      dropout=0.32,
  ):
      self.encoder = Encoder(
          input_features,
          enc_hidden_size,
          pre_enc_layers,
          post_enc_layers,
          stack_time_factor,
          dropout,
      )
      self.prediction = Prediction(
          vocab_size, pred_hidden_size, pred_layers, dropout
      )
      self.joint = Joint(
          vocab_size,
          pred_hidden_size,
          enc_hidden_size,
          joint_hidden_size,
          dropout,
      )
  
imports: from tinygrad.tensor import Tensor
  from tinygrad.jit import TinyJit
  from tinygrad.nn import Linear, Embedding
  from tinygrad.helpers import fetch
  import numpy
  from pathlib import Path
label_desc: Use image node_id 1 to create a new RNNT object  with example: obj = RNNT(input_features, vocab_size, enc_hidden_size, pred_hidden_size, joint_hidden_size, pre_enc_layers, post_enc_layers, pred_layers, stack_time_factor, dropout)