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: Train and fit the model on the datasets
  
  Args:
      train_set (:obj:`numpy.ndarray`): The train set
      args: callback_args and epochs from ArgParser input
code: def fit(self, train_set):
      """
      Train and fit the model on the datasets
  
      Args:
          train_set (:obj:`numpy.ndarray`): The train set
          args: callback_args and epochs from ArgParser input
      """
      self.model.fit(
          train_set["X"],
          train_set["y"],
          epochs=self.epochs,
          batch_size=self.batch_size,
          verbose=2,
      )
  
file_path: nlp-architect/nlp_architect/models/np_semantic_segmentation.py
imports: import tensorflow
label_desc: Use image node_id 3 for calling the NpSemanticSegClassifier obj's underlying member method code with example usage: obj.fit(train_set) without return types