import tensorflow as tf

        else:
          covariance_matrix = tf.convert_to_tensor(
              covariance_matrix, name="covariance_matrix", dtype=dtype)
          if validate_args:
            covariance_matrix = control_flow_ops.with_dependencies([
                tf.assert_near(
                    covariance_matrix,
                    tf.matrix_transpose(covariance_matrix),
                    message="Matrix was not symmetric")
            ], covariance_matrix)
          # No need to validate that covariance_matrix is non-singular.
          # LinearOperatorLowerTriangular has an assert_non_singular method that
          # is called by the Bijector.
          # However, cholesky() ignores the upper triangular part, so we do need
          # to separately assert symmetric.
          scale_tril = tf.cholesky(covariance_matrix)
        super(MultivariateNormalFullCovariance, self).__init__(
            loc=loc,
            scale_tril=scale_tril,
            validate_args=validate_args,
            allow_nan_stats=allow_nan_stats,
            name=name)
    self._parameters = parameters
