code: def classify_collocation(
      test_set, model_file_path, num_epochs, callback_args=None
  ):
      """
      Classify the dataset by the given trained model
  
      Args:
          model_file_path (str): model path
          num_epochs (int): number of epochs
          callback_args (dict): callback_arg
          test_set (:obj:`core_models.np_semantic_segmentation.data.NpSemanticSegData`):
              NpSemanticSegData object containing the dataset
  
      Returns:
          the output of the final layer for the entire Dataset
      """
      # load existing model
      if not os.path.isabs(model_file_path):
          # handle case using default value\relative paths
          model_file_path = os.path.join(
              os.path.dirname(__file__), model_file_path
          )
      loaded_model = NpSemanticSegClassifier(num_epochs, callback_args)
      loaded_model.load(model_file_path)
      print("Model loaded")
      # arrange the data
      return loaded_model.get_outputs(test_set["X"])
  
file_path: nlp-architect/examples/np_semantic_segmentation/inference.py
imports: import argparse
  import csv
  import os
  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
label_desc: Use image node_id 1 for calling a global function with example usage: classify_collocation(test_set, model_file_path, num_epochs, callback_args) and returns: loaded_model