class_docstr: The model used for text classification with WordEmbedding as its first layer.
  
  # Arguments
  class_num: The number of text categories to be classified. Positive int.
  embedding_file: The path to the word 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 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.
  sequence_length: The length of a sequence. Positive int. Default is 500.
  encoder: The encoder for input sequences. String. 'cnn' or 'lstm' or 'gru' are supported.
           Default is 'cnn'.
  encoder_output_dim: The output dimension for the encoder. Positive int. Default is 256.
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docstr: Evaluate on TextSet.
docstr_tok: ["Evaluate","on","TextSet","."]
code: def evaluate(self, x, batch_size=32):
      """
      Evaluate on TextSet.
      """
      assert isinstance(x, TextSet), "x should be a TextSet"
      return callZooFunc(
          self.bigdl_type,
          "textClassifierEvaluate",
          self.value,
          x,
          batch_size,
      )
  
file_path: analytics-zoo/pyzoo/zoo/models/textclassification/text_classifier.py
imports: import warnings
  from zoo.pipeline.api.keras.models import Sequential
  from zoo.pipeline.api.keras.layers import *
  from zoo.models.common import ZooModel
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 8 for calling the TextClassifier obj's underlying member method code with example usage: obj.evaluate(x, batch_size) and returns: callZooFunc