code: def most_common_word_train(x_train, y_train, x_valid, y_valid):
      """
      Train an MLP model, save it and evaluate it
  
      Args:
          x_train: x data for training
          y_train: y data for training
          x_valid: x data for validation
          y_valid: x data for validation
  
      Returns:
          str: reslts, predicted values by the model
  
      """
  
      # train set
      x_train = np.array(x_train)
      y_train1 = np.array(y_train)
      train_set = {"X": x_train, "y": y_train1}
  
      # validation set
      x_valid = np.array(x_valid)
      y_valid1 = np.array(y_valid)
      valid_set = {"X": x_valid, "y": y_valid1}
  
      input_dim = train_set["X"].shape[1]
      mlp_model = MostCommonWordSense(
          args.epochs, args.batch_size, None
      )
      # build model
      mlp_model.build(input_dim)
      # train
      mlp_model.fit(train_set)
      # save model
      mlp_model.save(args.model)
  
      # evaluation
      error_rate = mlp_model.eval(valid_set)
      logger.info(
          "Mis-classification error on validation set= %0.1f",
          error_rate * 100,
      )
  
      reslts = mlp_model.get_outputs(valid_set["X"])
  
      return reslts
  
imports: import argparse
  import logging
  import pickle
  import numpy
  from nlp_architect.models.most_common_word_sense import MostCommonWordSense
  from nlp_architect.utils.io import validate_existing_filepath, validate_parent_exists, check_size
label_desc: Use image node_id 1 for calling a global function with example usage: most_common_word_train(x_train, y_train, x_valid, y_valid) and returns: reslts