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: Args:
      num_epochs(int): number of epochs to train the model
      callback_args (dict): callback args keyword arguments to init Callback for the model
      loss: the model's loss function. Default is 'tf.keras.losses.binary_crossentropy' loss
      optimizer (:obj:`tf.keras.optimizers`): the model's optimizer. Default is `adam`
      batch_size (int):  batch size
code: def __init__(
      self,
      num_epochs,
      callback_args,
      loss="binary_crossentropy",
      optimizer="adam",
      batch_size=128,
  ):
      """
      Args:
          num_epochs(int): number of epochs to train the model
          callback_args (dict): callback args keyword arguments to init Callback for the model
          loss: the model's loss function. Default is 'tf.keras.losses.binary_crossentropy' loss
          optimizer (:obj:`tf.keras.optimizers`): the model's optimizer. Default is `adam`
          batch_size (int):  batch size
      """
      self.model = None
      self.loss = loss
      self.optimizer = optimizer
      self.epochs = num_epochs
      self.callback_args = callback_args
      self.batch_size = batch_size
  
file_path: nlp-architect/nlp_architect/models/np_semantic_segmentation.py
imports: import tensorflow
label_desc: Use image node_id 1 to create a new NpSemanticSegClassifier object  with example: obj = NpSemanticSegClassifier(num_epochs, callback_args, loss, optimizer, batch_size)