class_docstr: Embedding layer that directly loads pre-trained word vectors as weights.
  Turn non-negative integers (indices) into dense vectors of fixed size.
  Currently only GloVe embedding is supported.
  The input of this layer should be 2D.
  
  This layer can only be used as the first layer in a model, you need to provide the argument
  input_length (an integer) or input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  embedding_file: The path to the embedding file.
                  Currently the following GloVe files are supported:
                  "glove.6B.50d.txt", "glove.6B.100d.txt", "glove.6B.200d.txt"
                  "glove.6B.300d.txt", "glove.42B.300d.txt", "glove.840B.300d.txt".
                  You can download them from: https://nlp.stanford.edu/projects/glove/.
  word_index: Dictionary of word (string) and its corresponding index (int).
              The index is supposed to start from 1 with 0 reserved for unknown words.
              During the prediction, if you have words that are not in the word_index
              for the training, you can map them to index 0.
              Default is None. In this case, all the words in the embedding_file will
              be taken into account and you can call
              WordEmbedding.get_word_index(embedding_file) to retrieve the dictionary.
  trainable: To configure whether the weights of this layer will be updated or not.
             Only False is supported for now.
  input_length: Positive int. The sequence length of each input.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
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docstr: Get the full wordIndex map from the given embedding_file.
  
  # Arguments
  embedding_file: The path to the embedding file.
                  Currently only the following GloVe files are supported:
                  "glove.6B.50d.txt", "glove.6B.100d.txt", "glove.6B.200d.txt"
                  "glove.6B.300d.txt", "glove.42B.300d.txt", "glove.840B.300d.txt".
                  You can download them from: https://nlp.stanford.edu/projects/glove/.
  
  # Return
  Dictionary of word (string) and its corresponding index (int) obtained from
  the given embedding file.
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code: def get_word_index(embedding_file, bigdl_type="float"):
      """
      Get the full wordIndex map from the given embedding_file.
  
      # Arguments
      embedding_file: The path to the embedding file.
                      Currently only the following GloVe files are supported:
                      "glove.6B.50d.txt", "glove.6B.100d.txt", "glove.6B.200d.txt"
                      "glove.6B.300d.txt", "glove.42B.300d.txt", "glove.840B.300d.txt".
                      You can download them from: https://nlp.stanford.edu/projects/glove/.
  
      # Return
      Dictionary of word (string) and its corresponding index (int) obtained from
      the given embedding file.
      """
      return callZooFunc(
          bigdl_type, "wordEmbeddingGetWordIndex", embedding_file
      )
  
file_path: BigDL/python/dllib/src/bigdl/dllib/keras/layers/embeddings.py
imports: import sys
  from bigdl.dllib.utils.common import JTensor
  from bigdl.dllib.utils.file_utils import callZooFunc
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 2 for calling the WordEmbedding obj's underlying member method code with example usage: obj.get_word_index(embedding_file, bigdl_type) and returns: callZooFunc