class_docstr: Match texts model with Match Pyramid.
class_docstr_tok: ["Match","texts","model","with","Match","Pyramid","."]
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"]
  
      input_x_left_len = inputs["input_x_left_len"]
      input_x_right_len = inputs["input_x_right_len"]
  
      embedding = self.embed
      embed_left = embedding(input_left)
      embed_right = embedding(input_right)
  
      p_index = self._dynamic_pooling_index(
          input_x_left_len,
          input_x_right_len,
          self.max_seq_len,
          self.max_seq_len,
          1,
          1,
      )
  
      embed_cross = self.matching_layer([embed_left, embed_right])
      for i in range(self.num_blocks):
          embed_cross = self.conv[i](embed_cross)
      embed_pool = self.dpool([embed_cross, p_index])
  
      embed_flat = self.flatten(embed_pool)
  
      dropout = self.dropout(embed_flat)
      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 MatchPyramidTextClassModel obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: scores