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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def combine(self, expert_out, multiply_by_gates=True):
 """Sum together the expert output, weighted by the gates. The slice corresponding to a particular batch element `b` is computed as the sum over all experts `i` of the expert output, weighted by the corresponding gate values. If `multiply_by_gates` is set to False, the gate values are ignored. Args: expert_out: a list of `num_experts` `Tensor`s, each with shape `[expert_batch_size_i, <extra_output_dims>]`. multiply_by_gates: a boolean Returns: a `Tensor` with shape `[batch_size, <extra_output_dims>]`. """

    # see comments on convert_gradient_to_tensor
    stitched = common_layers.convert_gradient_to_tensor(
        tf.concat(expert_out, 0))
    if multiply_by_gates:
      stitched *= tf.expand_dims(self._nonzero_gates, 1)
    combined = tf.unsorted_segment_sum(stitched, self._batch_index,
                                       tf.shape(self._gates)[0])
    return combined