class_docstr: Bi-LSTM NER model with CRF classification layer (tf.keras model)
  
  Args:
      use_cudnn (bool, optional): use cudnn LSTM cells
docstr: Load word embedding weights into the model embedding layer
  
  Args:
      weights (numpy.ndarray): 2D matrix of word weights
docstr_tok: ["Load","word","embedding","weights","into","the","model","embedding","layer","Args",":","weights","(","numpy.ndarray",")",":","2D","matrix","of","word","weights"]
code: def load_embedding_weights(self, weights):
      """
      Load word embedding weights into the model embedding layer
  
      Args:
          weights (numpy.ndarray): 2D matrix of word weights
      """
      assert self.model is not None, (
          "Cannot assign weights, apply build() before trying to "
          "loading embedding weights "
      )
      emb_layer = self.model.get_layer(name="word_embedding")
      assert (
          emb_layer.output_dim == weights.shape[1]
      ), "embedding vectors shape mismatch"
      emb_layer.set_weights([weights])
  
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 4 for calling the NERCRF obj's underlying member method code with example usage: obj.load_embedding_weights(weights) without return types