tensorflow_lattice/python/pwl_calibration_layer.py [474:490]:
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      result = tf.reduce_sum(
          interpolation_weights * tf.transpose(bias_and_heights), axis=-1)
    else:
      # Single dim input has interpolation shape [batch_size, weights].
      result = tf.matmul(interpolation_weights, bias_and_heights)

    if self.impute_missing:
      if is_missing is None:
        if self.missing_input_value is None:
          raise ValueError("PWLCalibration layer is configured to impute "
                           "missing but no 'missing_input_value' specified and "
                           "'is_missing' tensor is not given.")
        assert self._missing_input_value_tensor is not None
        is_missing = tf.cast(
            tf.equal(inputs, self._missing_input_value_tensor),
            dtype=self.dtype)
      result = is_missing * self.missing_output + (1.0 - is_missing) * result
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tensorflow_lattice/python/pwl_calibration_sonnet_module.py [347:363]:
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      result = tf.reduce_sum(
          interpolation_weights * tf.transpose(bias_and_heights), axis=-1)
    else:
      # Single dim input has interpolation shape [batch_size, weights].
      result = tf.matmul(interpolation_weights, bias_and_heights)

    if self.impute_missing:
      if is_missing is None:
        if self.missing_input_value is None:
          raise ValueError("PWLCalibration layer is configured to impute "
                           "missing but no 'missing_input_value' specified and "
                           "'is_missing' tensor is not given.")
        assert self._missing_input_value_tensor is not None
        is_missing = tf.cast(
            tf.equal(inputs, self._missing_input_value_tensor),
            dtype=self.dtype)
      result = is_missing * self.missing_output + (1.0 - is_missing) * result
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