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: Save the model's prm file in model_path location
  
  Args:
      model_path(str): local path for saving the model
code: def save(self, model_path):
      """
      Save the model's prm file in model_path location
  
      Args:
          model_path(str): local path for saving the model
      """
      # serialize model to JSON
      model_json = self.model.to_json()
      with open(model_path[:-2] + "json", "w") as json_file:
          json_file.write(model_json)
      # serialize weights to HDF5
      self.model.save_weights(model_path)
      print("Saved model to disk")
  
file_path: nlp-architect/nlp_architect/models/np_semantic_segmentation.py
imports: import tensorflow
label_desc: Use image node_id 4 for calling the NpSemanticSegClassifier obj's underlying member method code with example usage: obj.save(model_path) without return types