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 predict(self, xs, softmax=False, argmax=False):
      concat_encodings = F.dropout(self.encoder(xs), ratio=self.dropout)
      concat_outputs = self.output(concat_encodings)
      if softmax:
          return F.softmax(concat_outputs).array
      elif argmax:
          return self.xp.argmax(concat_outputs.array, axis=1)
      else:
          return concat_outputs
  
imports: import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import reporter
label_desc: Use image node_id 3 for calling the TextClassifier obj's underlying member method code with example usage: obj.predict(xs, softmax, argmax) and returns: F, self, concat_outputs