class_docstr: A classifier using a given encoder.
  
  This chain encodes a sentence and classifies it into classes.
  
  Args:
      encoder (Link): A callable encoder, which extracts a feature.
          Input is a list of variables whose shapes are
          "(sentence_length, )".
          Output is a variable whose shape is "(batchsize, n_units)".
      n_class (int): The number of classes to be predicted.
code: def __init__(self, encoder, n_class, dropout=0.1):
      super(TextClassifier, self).__init__()
      with self.init_scope():
          self.encoder = encoder
          self.output = L.Linear(encoder.out_units, n_class)
      self.dropout = dropout
  
imports: import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import reporter
label_desc: Use image node_id 1 to create a new TextClassifier object from inherited base classes: chainer with example: obj = TextClassifier(encoder, n_class, dropout)