code: def train_with_forward_forward_algorithm(
      n_layers: int = 2,
      model_type: str = "progressive",
      device: str = "cpu",
      hidden_size: int = 2000,
      lr: float = 0.03,
      epochs: int = 100,
      batch_size: int = 5000,
      theta: float = 2.0,
      shuffle: bool = True,
      **kwargs,
  ):
      model_type = ForwardForwardModelType(model_type)
      root_op = ForwardForwardRootOp(model_type)
  
      output_size = None
      if model_type is ForwardForwardModelType.PROGRESSIVE:
          input_size = 28 * 28 + len(datasets.MNIST.classes)
      elif model_type is ForwardForwardModelType.RECURRENT:
          input_size = 28 * 28
          output_size = len(datasets.MNIST.classes)
      else:  # model_type is ForwardForwardModelType.NLP
          input_size = 10  # number of characters
          output_size = 30  # length of vocabulary
          assert (
              kwargs.get("predicted_tokens") is not None
          ), "predicted_tokens must be specified for NLP model"
  
      root_op.execute(
          input_size=input_size,
          n_layers=n_layers,
          hidden_size=hidden_size,
          optimizer_name="Adam",
          optimizer_params={"lr": lr},
          loss_fn_name="alternative_loss_fn",
          batch_size=batch_size,
          epochs=epochs,
          device=device,
          shuffle=shuffle,
          theta=theta,
          output_size=output_size,
      )
  
      return root_op.get_result()
  
label_desc: Use image node_id 1 for calling a global function with example usage: train_with_forward_forward_algorithm(n_layers, model_type, device, hidden_size, lr, epochs, batch_size, theta, shuffle) and returns: root_op