class_docstr: Match texts model with Rnn and Attention.
class_docstr_tok: ["Match","texts","model","with","Rnn","and","Attention","."]
code: def call(
      self, inputs, training=None, mask=None
  ):  # pylint: disable=too-many-locals
      input_left = inputs["input_x_left"]
      input_right = inputs["input_x_right"]
  
      embedding = self.embed
      embed_left = embedding(input_left)
      embed_right = embedding(input_right)
  
      encoded_left = self.lstm_left(embed_left)
      encoded_right = self.lstm_right(embed_right)
  
      encoded_right = tf.transpose(encoded_right, [0, 2, 1])
      left_right_sim = tf.matmul(encoded_left, encoded_right)
      shape_list = left_right_sim.get_shape()
      newdim = shape_list[1] * shape_list[2]
      sim_matrix = tf.reshape(
          left_right_sim, [-1, newdim], name="sim_matrix"
      )
  
      dropout = self.dropout(sim_matrix)
      out = self.outlayer(dropout)
  
      scores = self.final_dense(out)
  
      return scores
  
file_path: delta/delta/models/text_match_model.py
imports: import pickle
  from absl import logging
  import delta.compat
  from delta.layers.dynamic_pooling import DynamicPoolingLayer
  from delta.layers.match_pyramid import MatchingLayer
  from delta.models.base_model import Model
  from delta.utils.register import registers
label_desc: Use image node_id 2 for calling the MatchRnnTextClassModel obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: scores