code: def train_mlp_classifier(
      dataset, model_file_path, epochs, callback_args=None
  ):
      """
      Train the np_semantic_segmentation mlp classifier
      Args:
          model_file_path (str): model path
          epochs (int): number of epochs
          callback_args (dict): callback_arg
          dataset: NpSemanticSegData object containing the dataset
  
      Returns:
          print error_rate, test_accuracy_rate and precision_recall_rate evaluation from the model
  
      """
      model = NpSemanticSegClassifier(epochs, callback_args)
      input_dim = dataset.train_set_x.shape[1]
      model.build(input_dim)
      # run fit
      model.fit(dataset.train_set)
      # save model params
      model.save(model_file_path)
      # set evaluation error rates
      loss, binary_accuracy, precision, recall, f1 = model.eval(
          dataset.test_set
      )
      print("loss = %.1f%%" % (loss))
      print(
          "Test binary_accuracy rate = %.1f%%" % (binary_accuracy * 100)
      )
      print("Test precision rate = %.1f%%" % (precision * 100))
      print("Test recall rate = %.1f%%" % (recall * 100))
      print("Test f1 rate = %.1f%%" % (f1 * 100))
  
imports: import argparse
  from .data import NpSemanticSegData, absolute_path
  from nlp_architect.models.np_semantic_segmentation import NpSemanticSegClassifier
  from nlp_architect.utils.io import validate_existing_filepath, validate_parent_exists, validate
label_desc: Use image node_id 1 for calling a global function with example usage: train_mlp_classifier(dataset, model_file_path, epochs, callback_args) without return types