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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_slices(self, slices, tensor_shape, device=None):
 """Turns a set of slices into a single tensor. Args: slices: list of tf.Tensor with length self.size. tensor_shape: Shape. device: optional str. If absent, we use the devices of the slices. Returns: tf.Tensor. """

    if tensor_shape.ndims == 0:
      return slices[0]

    ret = slices[:]
    tensor_layout = self.tensor_layout(tensor_shape)
    for mesh_dim, tensor_axis in zip(
        self.shape, tensor_layout.mesh_axis_to_tensor_axis(self.ndims)):
      slice_size = len(ret) // mesh_dim.size
      if tensor_axis is None:
        ret = ret[:slice_size]
      else:
        if device:
          devices = [device] * slice_size
        else:
          devices = [ret[i].device for i in xrange(slice_size)]
        concat_inputs = []
        for i in xrange(slice_size):
          concat_inputs.append(
              [ret[i + slice_size * j] for j in xrange(mesh_dim.size)])
        ret = parallel(
            devices, tf.concat, concat_inputs,
            axis=[tensor_axis] * len(devices))
    assert len(ret) == 1
    return ret[0]