class_docstr: Bi-LSTM NER model with CRF classification layer (tf.keras model)
  
  Args:
      use_cudnn (bool, optional): use cudnn LSTM cells
docstr: Get the prediction of the model on given input
  
  Args:
      x (numpy.ndarray or :obj:`numpy.ndarray`): input samples
      batch_size (:obj:`int`, optional): batch size
  
  Returns:
      numpy.ndarray: predicted values by the model
docstr_tok: ["Get","the","prediction","of","the","model","on","given","input","Args",":","x","(","numpy.ndarray","or",":","obj",":","`","numpy.ndarray","`",")",":","input","samples","batch_size","(",":","obj",":","`","int","`",",","optional",")",":","batch","size","Returns",":","numpy.ndarray",":","predicted","values","by","the","model"]
code: def predict(self, x, batch_size=1):
      """
      Get the prediction of the model on given input
  
      Args:
          x (numpy.ndarray or :obj:`numpy.ndarray`): input samples
          batch_size (:obj:`int`, optional): batch size
  
      Returns:
          numpy.ndarray: predicted values by the model
      """
      assert self.model, "Model was not initialized"
      return self.model.predict(x, batch_size=batch_size)
  
file_path: nlp-architect/nlp_architect/models/ner_crf.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import tensorflow
  from nlp_architect.nn.tensorflow.python.keras.layers.crf import CRF
  from nlp_architect.nn.tensorflow.python.keras.utils import load_model, save_model
label_desc: Use image node_id 6 for calling the NERCRF obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: self