class_docstr: NP Semantic Segmentation classifier model (based on tf.Keras framework).
  
  Args:
      num_epochs(int): number of epochs to train the model
      **callback_args (dict): callback args keyword arguments to init a Callback for the model
      loss: the model's cost function. Default is 'tf.keras.losses.binary_crossentropy' loss
      optimizer (:obj:`tf.keras.optimizers`): the model's optimizer. Default is 'adam'
docstr: Load pre-trained model's .h5 file to NpSemanticSegClassifier object
  
  Args:
      model_path(str): local path for loading the model
code: def load(self, model_path):
      """
      Load pre-trained model's .h5 file to NpSemanticSegClassifier object
  
      Args:
          model_path(str): local path for loading the model
      """
      # load json and create model
      with open(model_path[:-2] + "json", "r") as json_file:
          loaded_model_json = json_file.read()
      loaded_model = tf.keras.models.model_from_json(loaded_model_json)
      # load weights into new model
      loaded_model.load_weights(model_path)
      print("Loaded model from disk")
      self.model = loaded_model
  
file_path: nlp-architect/nlp_architect/models/np_semantic_segmentation.py
imports: import tensorflow
label_desc: Use image node_id 5 for calling the NpSemanticSegClassifier obj's underlying member method code with example usage: obj.load(model_path) without return types