Bug fixes

    input_shape = X.shape

    X = np.reshape(X, [1] + list(X.shape))

    self._create_inputs([X])

    X, _, _ = self._prepare_batch((X, None, None))

    X, _, _ = self._prepare_batch(([X], None, None))

    if tf.executing_eagerly():

      # In eager mode we use a GradientTape to compute gradients.

      X = tf.constant(X)

      X = tf.constant(X[0])

      with tf.GradientTape(

          persistent=True, watch_accessed_variables=False) as tape:

        tape.watch(X)

          jacobian(self._output_tensors[i], self._input_placeholders[0])

          for i in self._prediction_outputs

      ]

      feed_dict = {self._input_placeholders[0]: X}

      feed_dict = {self._input_placeholders[0]: X[0]}

      result = self.session.run(grads, feed_dict=feed_dict)

      output_shapes = [

          tuple(o.shape.as_list()[1:]) for o in self._output_tensors

    losses = self.loss(outputs[0], labels[0])

    w = weights[0]

    if len(w.shape) < len(losses.shape):

      if isinstance(w, tf.Tensor):

        shape = tuple(w.shape.as_list())

      else:

        shape = w.shape

      w = tf.reshape(w, shape + (1,) * (len(losses.shape) - len(w.shape)))

    loss = losses * w

    return tf.reduce_mean(loss) + sum(self.model.losses)