code: def load_from_pretrained(self):
      fn = Path(__file__).parents[1] / "weights/rnnt.pt"
      fetch(
          "https://zenodo.org/record/3662521/files/DistributedDataParallel_1576581068.9962234-epoch-100.pt?download=1",
          fn,
      )
  
      import torch
  
      with open(fn, "rb") as f:
          state_dict = torch.load(f, map_location="cpu")["state_dict"]
  
      # encoder
      for i in range(2):
          self.encoder.pre_rnn.cells[i].weights_ih.assign(
              state_dict[f"encoder.pre_rnn.lstm.weight_ih_l{i}"].numpy()
          )
          self.encoder.pre_rnn.cells[i].weights_hh.assign(
              state_dict[f"encoder.pre_rnn.lstm.weight_hh_l{i}"].numpy()
          )
          self.encoder.pre_rnn.cells[i].bias_ih.assign(
              state_dict[f"encoder.pre_rnn.lstm.bias_ih_l{i}"].numpy()
          )
          self.encoder.pre_rnn.cells[i].bias_hh.assign(
              state_dict[f"encoder.pre_rnn.lstm.bias_hh_l{i}"].numpy()
          )
      for i in range(3):
          self.encoder.post_rnn.cells[i].weights_ih.assign(
              state_dict[
                  f"encoder.post_rnn.lstm.weight_ih_l{i}"
              ].numpy()
          )
          self.encoder.post_rnn.cells[i].weights_hh.assign(
              state_dict[
                  f"encoder.post_rnn.lstm.weight_hh_l{i}"
              ].numpy()
          )
          self.encoder.post_rnn.cells[i].bias_ih.assign(
              state_dict[f"encoder.post_rnn.lstm.bias_ih_l{i}"].numpy()
          )
          self.encoder.post_rnn.cells[i].bias_hh.assign(
              state_dict[f"encoder.post_rnn.lstm.bias_hh_l{i}"].numpy()
          )
  
      # prediction
      self.prediction.emb.weight.assign(
          state_dict["prediction.embed.weight"].numpy()
      )
      for i in range(2):
          self.prediction.rnn.cells[i].weights_ih.assign(
              state_dict[
                  f"prediction.dec_rnn.lstm.weight_ih_l{i}"
              ].numpy()
          )
          self.prediction.rnn.cells[i].weights_hh.assign(
              state_dict[
                  f"prediction.dec_rnn.lstm.weight_hh_l{i}"
              ].numpy()
          )
          self.prediction.rnn.cells[i].bias_ih.assign(
              state_dict[
                  f"prediction.dec_rnn.lstm.bias_ih_l{i}"
              ].numpy()
          )
          self.prediction.rnn.cells[i].bias_hh.assign(
              state_dict[
                  f"prediction.dec_rnn.lstm.bias_hh_l{i}"
              ].numpy()
          )
  
      # joint
      self.joint.l1.weight.assign(
          state_dict["joint_net.0.weight"].numpy()
      )
      self.joint.l1.bias.assign(state_dict["joint_net.0.bias"].numpy())
      self.joint.l2.weight.assign(
          state_dict["joint_net.3.weight"].numpy()
      )
      self.joint.l2.bias.assign(state_dict["joint_net.3.bias"].numpy())
  
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 6 for calling the RNNT obj's underlying member method code with example usage: obj.load_from_pretrained() without return types