def quantiles_ready():
    """The subgraph for when the quantiles are ready."""
    quantized_feature = quantile_ops.quantiles([sparse_column_values], [],
                                               [quantile_buckets], [])
    quantized_feature = math_ops.cast(quantized_feature[0], dtypes.int64)
    quantized_feature = array_ops.reshape(quantized_feature, [-1])
    example_indices, _ = array_ops.split(
        sparse_column_indices, num_or_size_splits=2, axis=1)
    example_indices = array_ops.squeeze(example_indices, [1])
    filtered_gradients = array_ops.gather(gradients, example_indices)
    filtered_hessians = array_ops.gather(hessians, example_indices)
    filtered_partition_ids = array_ops.gather(example_partition_ids,
                                              example_indices)
    unique_partitions, mapped_partitions = array_ops.unique(
        example_partition_ids)

    # Compute aggregate stats for each partition.
    per_partition_gradients = math_ops.unsorted_segment_sum(
        gradients, mapped_partitions, array_ops.size(unique_partitions))
    per_partition_hessians = math_ops.unsorted_segment_sum(
        hessians, mapped_partitions, array_ops.size(unique_partitions))

    # Prepend a bias feature per partition that accumulates the stats for all
    # examples in that partition.
    bias_feature_ids = array_ops.fill(
        array_ops.shape(unique_partitions), _BIAS_FEATURE_ID)
    bias_feature_ids = math_ops.cast(bias_feature_ids, dtypes.int64)
    partition_ids = array_ops.concat(
        [unique_partitions, filtered_partition_ids], 0)
    filtered_gradients = array_ops.concat(
        [per_partition_gradients, filtered_gradients], 0)
    filtered_hessians = array_ops.concat(
        [per_partition_hessians, filtered_hessians], 0)
    bucket_ids = array_ops.concat([bias_feature_ids, quantized_feature], 0)
    return partition_ids, bucket_ids, filtered_gradients, filtered_hessians

  def _full_batch_training_op(self, inputs, cluster_idx_list, cluster_centers):
    """Creates an op for training for full batch case.

    Args:
      inputs: list of input Tensors.
      cluster_idx_list: A vector (or list of vectors). Each element in the
        vector corresponds to an input row in 'inp' and specifies the cluster id
        corresponding to the input.
      cluster_centers: Tensor Ref of cluster centers.

    Returns:
      An op for doing an update of mini-batch k-means.
    """
    cluster_sums = []
    cluster_counts = []
    epsilon = constant_op.constant(1e-6, dtype=inputs[0].dtype)
    for inp, cluster_idx in zip(inputs, cluster_idx_list):
      with ops.colocate_with(inp):
        cluster_sums.append(
            math_ops.unsorted_segment_sum(inp, cluster_idx, self._num_clusters))
        cluster_counts.append(
            math_ops.unsorted_segment_sum(
                array_ops.reshape(
                    array_ops.ones(
                        array_ops.reshape(array_ops.shape(inp)[0], [-1])),
                    [-1, 1]), cluster_idx, self._num_clusters))
    with ops.colocate_with(cluster_centers):
      new_clusters_centers = math_ops.add_n(cluster_sums) / (math_ops.cast(
          math_ops.add_n(cluster_counts), cluster_sums[0].dtype) + epsilon)
      if self._clusters_l2_normalized():
        new_clusters_centers = nn_impl.l2_normalize(new_clusters_centers, dim=1)
    return state_ops.assign(cluster_centers, new_clusters_centers)

    def active_inputs():
      """The normal flow when the handler is active."""
      # Remove the second column of example indices matrix since it is not
      # useful.
      example_indices, _ = array_ops.split(
          self._sparse_int_column.indices, num_or_size_splits=2, axis=1)
      example_indices = array_ops.squeeze(example_indices, [1])

      filtered_gradients = array_ops.gather(gradients, example_indices)
      filtered_hessians = array_ops.gather(hessians, example_indices)
      filtered_partition_ids = array_ops.gather(example_partition_ids,
                                                example_indices)
      unique_partitions, mapped_partitions = array_ops.unique(
          example_partition_ids)

      # Compute aggregate stats for each partition.
      # The bias is computed on gradients and hessians (and not
      # filtered_gradients) which have exactly one value per example, so we
      # don't double count a gradient in multivalent columns.
      # Since unsorted_segment_sum can be numerically unstable, use 64bit
      # operation.
      gradients64 = math_ops.cast(gradients, dtypes.float64)
      hessians64 = math_ops.cast(hessians, dtypes.float64)
      per_partition_gradients = math_ops.unsorted_segment_sum(
          gradients64, mapped_partitions, array_ops.size(unique_partitions))
      per_partition_hessians = math_ops.unsorted_segment_sum(
          hessians64, mapped_partitions, array_ops.size(unique_partitions))
      per_partition_gradients = math_ops.cast(per_partition_gradients,
                                              dtypes.float32)
      per_partition_hessians = math_ops.cast(per_partition_hessians,
                                             dtypes.float32)
      # Prepend a bias feature per partition that accumulates the stats for all
      # examples in that partition.
      # Bias is added to the stats even if there are no examples with values in
      # the current sparse column. The reason is that the other example batches
      # might have values in these partitions so we have to keep the bias
      # updated.
      bias_feature_ids = array_ops.fill(
          array_ops.shape(unique_partitions), _BIAS_FEATURE_ID)
      bias_feature_ids = math_ops.cast(bias_feature_ids, dtypes.int64)
      partition_ids = array_ops.concat(
          [unique_partitions, filtered_partition_ids], 0)
      filtered_gradients = array_ops.concat(
          [per_partition_gradients, filtered_gradients], 0)
      filtered_hessians = array_ops.concat(
          [per_partition_hessians, filtered_hessians], 0)
      feature_ids = array_ops.concat(
          [bias_feature_ids, self._sparse_int_column.values], 0)
      # Dimension is always zero for sparse int features.
      dimension_ids = array_ops.zeros_like(feature_ids, dtype=dtypes.int64)
      feature_ids_and_dimensions = array_ops.stack(
          [feature_ids, dimension_ids], axis=1)
      return (partition_ids, feature_ids_and_dimensions, filtered_gradients,
              filtered_hessians)

  def quantiles_ready():
    """The subgraph for when the quantiles are ready."""
    quantized_feature = quantile_ops.quantiles([], [sparse_column_values], [],
                                               [quantile_buckets],
                                               [sparse_column_indices])

    quantized_feature = math_ops.cast(quantized_feature[1], dtypes.int64)
    quantized_feature = array_ops.squeeze(quantized_feature, axis=0)

    example_indices, _ = array_ops.split(
        sparse_column_indices, num_or_size_splits=2, axis=1)
    example_indices = array_ops.squeeze(example_indices, [1])
    filtered_gradients = array_ops.gather(gradients, example_indices)
    filtered_hessians = array_ops.gather(hessians, example_indices)
    filtered_partition_ids = array_ops.gather(example_partition_ids,
                                              example_indices)
    unique_partitions, mapped_partitions = array_ops.unique(
        example_partition_ids)

    # Compute aggregate stats for each partition.
    # Since unsorted_segment_sum can be numerically unstable, use 64bit
    # operation.
    gradients64 = math_ops.cast(gradients, dtypes.float64)
    hessians64 = math_ops.cast(hessians, dtypes.float64)
    per_partition_gradients = math_ops.unsorted_segment_sum(
        gradients64, mapped_partitions, array_ops.size(unique_partitions))
    per_partition_hessians = math_ops.unsorted_segment_sum(
        hessians64, mapped_partitions, array_ops.size(unique_partitions))
    per_partition_gradients = math_ops.cast(per_partition_gradients,
                                            dtypes.float32)
    per_partition_hessians = math_ops.cast(per_partition_hessians,
                                           dtypes.float32)
    # Prepend a bias feature per partition that accumulates the stats for all
    # examples in that partition.
    bias_feature_ids = array_ops.fill(
        array_ops.shape(unique_partitions), _BIAS_FEATURE_ID)
    bias_feature_ids = math_ops.cast(bias_feature_ids, dtypes.int64)
    zeros = array_ops.zeros_like(bias_feature_ids)
    bias_feature_ids = array_ops.stack([bias_feature_ids, zeros], axis=1)

    partition_ids = array_ops.concat(
        [unique_partitions, filtered_partition_ids], 0)
    filtered_gradients = array_ops.concat(
        [per_partition_gradients, filtered_gradients], 0)
    filtered_hessians = array_ops.concat(
        [per_partition_hessians, filtered_hessians], 0)

    bucket_ids = array_ops.concat([bias_feature_ids, quantized_feature], 0)

    return partition_ids, bucket_ids, filtered_gradients, filtered_hessians

 def _apply_sparse(self, g_t, x_tm1, prepare):
   """"""
   
   idxs, idxs_ = array_ops.unique(g_t.indices)
   g_t_ = math_ops.unsorted_segment_sum(g_t.values, idxs_, array_ops.size(idxs))
   updates = []
   
   if self._mu > 0:
     m_and_t = self._sparse_moving_average(x_tm1, idxs, g_t_, 'm', self._mu)
     m_t_ = array_ops.gather(m_and_t[0], idxs)
     gamma_t = ops.convert_to_tensor(self._gamma)
     m_bar_t_ = (1-gamma_t)*m_t_ + gamma_t*g_t_
     updates.extend(m_and_t)
   else:
     m_bar_t_ = g_t_
   
   if self._ups > 0:
     v_and_t = self._sparse_moving_average(x_tm1, idxs, g_t_**2, 'v', self._ups)
     v_t_ = array_ops.gather(v_and_t[0], idxs)
     eps_t = ops.convert_to_tensor(self._eps)
     v_bar_t_ = math_ops.sqrt(v_t_ + eps_t)
     updates.extend(v_and_t)
   else:
     v_bar_t_ = 1.
   
   lr_t = ops.convert_to_tensor(self._lr)
   s_t_ = lr_t * m_bar_t_ / v_bar_t_
   return [[s_t_, x_tm1, idxs, g_t]] + updates

 def _prepare(self, grads_and_vars):
   """"""
   
   if self._lr is None:
     sTy = 0
     sTs = 0
     yTy = 0
     for g_t, x_tm1 in grads_and_vars:
       if g_t is None:
         continue
       with ops.name_scope('update_' + x_tm1.op.name), ops.device(x_tm1.device):
         if isinstance(g_t, ops.Tensor):
           g_tm1 = self.get_slot(x_tm1, 'g')
           s_tm1 = self.get_slot(x_tm1, 's')
           y_t = (g_t-g_tm1)
           sTy += math_ops.reduce_sum(s_tm1*y_t)
           sTs += math_ops.reduce_sum(s_tm1**2)
           yTy += math_ops.reduce_sum(y_t**2)
         else:
           idxs, idxs_ = array_ops.unique(g_t.indices)
           g_t_ = math_ops.unsorted_segment_sum(g_t.values, idxs_, array_ops.size(idxs))
           g_tm1 = self.get_slot(x_tm1, 'g')
           g_tm1_ = array_ops.gather(g_tm1, idxs)
           s_tm1 = self.get_slot(x_tm1, 's')
           s_tm1_ = array_ops.gather(s_tm1, idxs)
           y_t_ = (g_t_-g_tm1_)
           sTy += math_ops.reduce_sum(s_tm1_*y_t_)
           sTs += math_ops.reduce_sum(s_tm1_**2)
           yTy += math_ops.reduce_sum(y_t_**2)
     sTy = math_ops.abs(sTy)
     self._lr = sTs / (sTy + self._eps)

  def testAggregateGradients(self):

    def fn(x):
      ind1 = tensor.Tensor(np.array([0, 1]))
      ind2 = tensor.Tensor(np.array([2, 3]))
      ind3 = tensor.Tensor(np.array([1, 3]))
      # A mixture of IndexedSlices and dense tensor to aggregate.
      g1 = embedding_ops.embedding_lookup(x, ind1)
      g2 = embedding_ops.embedding_lookup(x, ind2)
      g3 = embedding_ops.embedding_lookup(x, ind3)
      g4 = math_ops.reduce_sum(x * tensor.Tensor(2.0))
      return g1 * g2 * g3 * g4

    var_np = np.random.rand(4, 2).astype(np.float32)
    var = tensor.Tensor(var_np)
    grad = backprop.gradients_function(fn, [0])(var)[0]

    with context.graph_mode(), self.test_session():
      tf_var = array_ops.constant(var_np, dtypes.float32)
      tf_ind1 = array_ops.constant([0, 1])
      tf_ind2 = array_ops.constant([2, 3])
      tf_ind3 = array_ops.constant([1, 3])
      tf_g1 = embedding_ops.embedding_lookup(tf_var, tf_ind1)
      tf_g2 = embedding_ops.embedding_lookup(tf_var, tf_ind2)
      tf_g3 = embedding_ops.embedding_lookup(tf_var, tf_ind3)
      tf_g4 = math_ops.reduce_sum(tf_var * 2.0, reduction_indices=(0, 1))
      tf_y = tf_g1 * tf_g2 * tf_g3 * tf_g4
      tf_grad = gradients.gradients(tf_y, [tf_var])[0]

      tf_dense_grad = math_ops.unsorted_segment_sum(
          tf_grad.values, tf_grad.indices, tf_grad.dense_shape[0])

      self.assertAllClose(grad.numpy(), tf_dense_grad.eval())

 def approximate_hessian(self, grads_and_vars, name=None):
   """
   I haven't tested this yet so I have no idea if it works, but even if it
   does it's probably super slow, and either way nothing else has been modified
   to deal with it.
   """
   
   gv = 0
   var_refs = []
   for g_t, x_tm1 in grads_and_vars:
     var_refs.append(x_tm1.ref())
     if g_t is None:
       continue
     with ops.name_scope('update_' + x_tm1.op.name), ops.device(x_tm1.device):
       if isinstance(g_t, ops.Tensor):
         gv += math_ops.reduce_sum(g_t * random_ops.random_normal(g_t.get_shape()))
       else:
         idxs, idxs_ = array_ops.unique(g_t.indices)
         g_t_ = math_ops.unsorted_segment_sum(g_t.values, idxs_, array_ops.size(idxs))
         gv += math_ops.reduce_sum(g_t_ * random_ops.random_normal(g_t_.get_shape()))
   hesses = gradients.gradients(gv, var_refs,
                                gate_gradients=(gate_gradients == Optimizer.GATE_OP),
                                aggregation_method=aggregation_method,
                                colocate_gradients_with_ops=colocate_gradients_with_ops)
   return zip([g_t for g_t, _ in grads_and_vars], [x_tm1 for _, x_tm1 in grads_and_vars], hesses)

 def _apply_sparse(self, grad, var):
   if len(grad.indices.get_shape()) == 1:
     grad_indices = grad.indices
     grad_values = grad.values
   else:
     grad_indices = array_ops.reshape(grad.indices, [-1])
     grad_values = array_ops.reshape(grad.values, [-1, grad.values.get_shape()[-1].value])
   gidxs, metagidxs = array_ops.unique(grad_indices)
   sizegidxs = array_ops.size(gidxs)
   gvals = math_ops.unsorted_segment_sum(grad_values, metagidxs, sizegidxs)
   # m_t = mu * m + (1 - mu) * g_t
   m = self.get_slot(var, "m")
   m_scaled_g_values = gvals * (1 - self._mu_t)
   m_t = state_ops.scatter_update(m, gidxs,
                                  array_ops.gather(m, gidxs) * self._mu_t,
                                  use_locking=self._use_locking)
   m_t = state_ops.scatter_add(m_t, gidxs, m_scaled_g_values,
                               use_locking=self._use_locking)
   m_t_ = array_ops.gather(m_t, gidxs) / (1 - self._mu2_t * self._mu_power)
   # m_bar = mu * m_t + (1 - mu) * g_t
   m_bar = self._mu2_t * m_t_ + m_scaled_g_values / (1 - self._mu_power)
   var_update = state_ops.scatter_sub(var, gidxs,
                                    self._lr_t * m_bar,
                                    use_locking=self._use_locking)
   return control_flow_ops.group(*[var_update, m_t])

    def DynamicStitchGrads(op, grad):
        num_values = len(op.inputs) // 2
        indices_grad = [None] * num_values

        def AsInt32(x):
            return (x if op.inputs[0].dtype == dtypes.int32 else
                    math_ops.cast(x, dtypes.int32))

        idxs = [AsInt32(array_ops.reshape(op.inputs[i], (-1,)))
                for i in range(num_values)]
        if isinstance(grad, ops.IndexedSlices):
            output_shape = array_ops.shape(op.outputs[0])
            output_rows = output_shape[0]
            grad = math_ops.unsorted_segment_sum(grad.values, grad.indices,
                                                 output_rows)

        values_grad = []
        zeros = array_ops.zeros_like(grad)
        idx_zeros = [zeros[:array_ops.shape(x)[0]] for x in idxs]
        grad_range = math_ops.range(array_ops.shape(grad)[0])
        for i in range(num_values):
            if i == num_values - 1:
                v_grad = grad
            else:
                v_grad = data_flow_ops.dynamic_stitch(
                    [grad_range] + idxs[i + 1:], [grad] + idx_zeros[i + 1:])
            v_grad = array_ops.gather(v_grad, AsInt32(op.inputs[i]))
            values_grad += [v_grad]

        return indices_grad + values_grad

  def testAggregateGradients(self):

    def fn(x):
      ind1 = constant_op.constant(np.array([0, 1]))
      ind2 = constant_op.constant(np.array([2, 3]))
      ind3 = constant_op.constant(np.array([1, 3]))
      # A mixture of IndexedSlices and dense tensor to aggregate.
      g1 = embedding_ops.embedding_lookup(x, ind1)
      g2 = embedding_ops.embedding_lookup(x, ind2)
      g3 = embedding_ops.embedding_lookup(x, ind3)
      g4 = math_ops.reduce_sum(x * constant_op.constant(2.0))
      return g1 * g2 * g3 * g4

    var_np = np.random.rand(4, 2).astype(np.float32)
    var = constant_op.constant(var_np)
    grad = backprop.gradients_function(fn, [0])(var)[0]
    grad = self.evaluate(ops.convert_to_tensor(grad))

    if not context.executing_eagerly():
      tf_var = array_ops.constant(var_np, dtypes.float32)
      tf_ind1 = array_ops.constant([0, 1])
      tf_ind2 = array_ops.constant([2, 3])
      tf_ind3 = array_ops.constant([1, 3])
      tf_g1 = embedding_ops.embedding_lookup(tf_var, tf_ind1)
      tf_g2 = embedding_ops.embedding_lookup(tf_var, tf_ind2)
      tf_g3 = embedding_ops.embedding_lookup(tf_var, tf_ind3)
      tf_g4 = math_ops.reduce_sum(tf_var * 2.0, axis=(0, 1))
      tf_y = tf_g1 * tf_g2 * tf_g3 * tf_g4
      tf_grad = gradients.gradients(tf_y, [tf_var])[0]

      tf_dense_grad = math_ops.unsorted_segment_sum(
          tf_grad.values, tf_grad.indices, tf_grad.dense_shape[0])

      self.assertAllClose(grad, self.evaluate(tf_dense_grad))

def _TileGrad(op, grad):
  """Sum reduces grad along the tiled dimensions."""
  input_shape = array_ops.shape(op.inputs[0])
  # We interleave multiples and input_shape to get split_shape,
  # reshape grad to split_shape, and reduce along all even
  # dimensions (the tiled dimensions) to get the result
  # with shape input_shape.  For example
  #   input_shape = [20, 30, 40]
  #   multiples = [2, 3, 4]
  #   split_shape = [2, 20, 3, 30, 4, 40]
  #   axes = [0, 2, 4]
  split_shape = array_ops.reshape(
      array_ops.transpose(array_ops.stack([op.inputs[1], input_shape])), [-1])
  axes = math_ops.range(0, array_ops.size(split_shape), 2)
  # Sum reduces grad along the first dimension for IndexedSlices
  if isinstance(grad, ops.IndexedSlices):
    grad = math_ops.unsorted_segment_sum(
        grad.values,
        math_ops.mod(grad.indices, input_shape[0]),
        input_shape[0])
    split_shape = array_ops.concat([[1], split_shape[1:]], axis=0)
  input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes)
  # Fix shape inference
  if not context.executing_eagerly():
    input_grad.set_shape(op.inputs[0].get_shape())
  return [input_grad, None]

  def testGradientMatchesSegmentSum(self):
    # Strategy: compute the gradient for UnsortedSegmentSum and SegmentSum
    # and compare the outputs, which should be identical.
    # NB: for this test to work, indices must be valid for SegmentSum, namely
    # it must be sorted, the indices must be contiguous, and num_segments
    # must be max(indices) + 1.
    indices = [0, 0, 1, 1, 1, 2, 3, 4, 5]
    n = len(indices)
    num_cols = 2
    shape = [n, num_cols]
    num_segments = max(indices) + 1
    for dtype in self.differentiable_dtypes:
      with self.cached_session(use_gpu=True):
        tf_x, np_x = self._input(shape, dtype=dtype)
        # Results from UnsortedSegmentSum
        unsorted_s = math_ops.unsorted_segment_sum(
            data=tf_x, segment_ids=indices, num_segments=num_segments)
        unsorted_jacob_t, unsorted_jacob_n = (
            gradient_checker.compute_gradient(tf_x, shape, unsorted_s,
                                              [num_segments, num_cols],
                                              x_init_value=np_x, delta=1))

        # Results from SegmentSum
        sorted_s = math_ops.segment_sum(data=tf_x, segment_ids=indices)
        sorted_jacob_t, sorted_jacob_n = gradient_checker.compute_gradient(
            tf_x,
            shape,
            sorted_s, [num_segments, num_cols],
            x_init_value=np_x,
            delta=1)
      self.assertAllClose(unsorted_jacob_t, sorted_jacob_t)
      self.assertAllClose(unsorted_jacob_n, sorted_jacob_n)

 def testDropNegatives(self):
   # Note: the test is done by replacing segment_ids with 8 to -1
   # for index  and replace values generated by numpy with 0.
   dtypes = [
       dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int64,
       dtypes_lib.int32, dtypes_lib.complex64, dtypes_lib.complex128
   ]
   indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3])
   num_segments = 12
   for indices in indices_flat, indices_flat.reshape(5, 2):
     shape = indices.shape + (2,)
     for dtype in dtypes:
       with self.test_session(use_gpu=True):
         tf_x, np_x = self._input(shape, dtype=dtype)
         np_ans = self._segmentReduce(
             indices, np_x, np.add, op2=None, num_segments=num_segments)
         # Replace np_ans[8] with 0 for the value
         np_ans[8:] = 0
         # Replace 8 with -1 in indices
         np.place(indices, indices == 8, [-1])
         s = math_ops.unsorted_segment_sum(
             data=tf_x, segment_ids=indices, num_segments=num_segments)
         tf_ans = s.eval()
       self.assertAllClose(np_ans, tf_ans)
       self.assertShapeEqual(np_ans, s)

def _GatherV2Grad(op, grad):
  """Gradient for GatherV2 op."""
  # params can be large, so colocate the shape calculation with it.
  #
  # params can be very large for sparse model, array_ops.shape raises
  # exception on the Windows platform when any dimension is larger than
  # int32. params_shape is not used in optimizer apply_sparse gradients,
  # so it's fine to convert it back to int32 regardless of truncation.
  params = op.inputs[0]
  with ops.colocate_with(params):
    params_shape = array_ops.shape(params, out_type=ops.dtypes.int64)
    params_shape = math_ops.to_int32(params_shape)

  indices = op.inputs[1]
  indices_size = array_ops.expand_dims(array_ops.size(indices), 0)
  axis = op.inputs[2]
  axis_static = tensor_util.constant_value(axis)

  # For axis 0 gathers, build an appropriately shaped IndexedSlices.
  if axis_static == 0:
    values_shape = array_ops.concat([indices_size, params_shape[1:]], 0)
    values = array_ops.reshape(grad, values_shape)
    indices = array_ops.reshape(indices, indices_size)
    return [ops.IndexedSlices(values, indices, params_shape), None, None]

  outer_shape = params_shape[:axis]
  outer_dims = array_ops.size(outer_shape)
  inner_shape = params_shape[axis:][1:]
  inner_dims = array_ops.size(inner_shape)

  outer_axes_indices = math_ops.range(outer_dims)
  inner_axes_indices = math_ops.range(outer_dims + 1,
                                      outer_dims + 1 + inner_dims)

  values_shape = array_ops.concat([outer_shape, indices_size, inner_shape], 0)
  values = array_ops.reshape(grad, values_shape)
  indices = array_ops.reshape(indices, indices_size)

  # We need to sum up every slice `values[..., i, ....]` corresponding to
  # `params[..., indices[i], ...]`. Since `unsorted_segment_sum` does not
  # support an axis parameter, we transpose the gather dimension to the front,
  # then use `unsorted_segment_sum` to build a
  # [gather_axis, outer_axes, inner_axes] tensor with all the gradients
  # affecting each index in `gather_axis` summed up.
  transpose_dims = array_ops.concat(
      [[outer_dims], outer_axes_indices, inner_axes_indices], 0)
  values_transpose = array_ops.transpose(values, transpose_dims)
  num_segments = params_shape[axis]

  params_grad = math_ops.unsorted_segment_sum(
      values_transpose, indices, num_segments)

  # Inverts the above transpose by moving dimension 0 back to its original
  # position.
  invert_transpose_dims = array_ops.concat(
      [outer_axes_indices + 1, [0], inner_axes_indices], 0)
  params_grad = array_ops.transpose(params_grad, invert_transpose_dims)
  return [params_grad, None, None]

 def testBadIndices(self):
   # Note: GPU kernel does not return the out-of-range error needed for this
   # test, so this test is marked as cpu-only.
   with self.test_session(use_gpu=False):
     for bad in [[-1]], [[7]]:
       unsorted = math_ops.unsorted_segment_sum([[17]], bad, num_segments=2)
       with self.assertRaisesOpError(
           r"segment_ids\[0,0\] = %d is out of range \[0, 2\)" % bad[0][0]):
         unsorted.eval()

 def testBadIndices(self):
   # Note: GPU kernel does not return the out-of-range error needed for this
   # test, so this test is marked as cpu-only.
   # Note: With PR #13055 a negative index will be ignored silently.
   with self.session(use_gpu=False):
     for bad in [[2]], [[7]]:
       unsorted = math_ops.unsorted_segment_sum([[17]], bad, num_segments=2)
       with self.assertRaisesOpError(
           r"segment_ids\[0,0\] = %d is out of range \[0, 2\)" % bad[0][0]):
         self.evaluate(unsorted)

 def testEmptySecondDimension(self):
   dtypes = [np.float16, np.float32, np.float64, np.int64, np.int32,
             np.complex64, np.complex128]
   with self.session(use_gpu=True):
     for dtype in dtypes:
       for itype in (np.int32, np.int64):
         data = np.zeros((2, 0), dtype=dtype)
         segment_ids = np.array([0, 1], dtype=itype)
         unsorted = math_ops.unsorted_segment_sum(data, segment_ids, 2)
         self.assertAllEqual(unsorted.eval(), np.zeros((2, 0), dtype=dtype))

 def UnsortedSegmentSum(self, data, indices, num_segments):
   with self.test_session() as sess, self.test_scope():
     d = array_ops.placeholder(data.dtype, shape=data.shape)
     if isinstance(indices, int):
       i = array_ops.placeholder(np.int32, shape=[])
     else:
       i = array_ops.placeholder(indices.dtype, shape=indices.shape)
     return sess.run(
         math_ops.unsorted_segment_sum(d, i, num_segments),
         {d: data,
          i: indices})

  def _aggregate_sparse_grad(self, grad, var, train_ops):
    """Aggregate sparse gradients.

    Args:
      grad: The sparse gradient to aggregate.
      var: The variable to apply this gradient to.
      train_ops: The train_ops for the worker to run.

    Returns:
      aggregated_grad: Aggregated grad.
    """
    # Sparse gradients have to be inserted as one pair of (value,
    # indice) as an element instead of the whole "indexedslice" because
    # their shapes are not deterministic.
    sparse_grad_queue = (data_flow_ops.FIFOQueue(
        -1,
        (grad.values.dtype, grad.indices.dtype),
        shapes=(var.get_shape().as_list()[1:], ()),
        shared_name="sparse_grad_q_%s" % var.name))
    self._sparse_grad_queues_and_devs.append((sparse_grad_queue, var.device))

    # Sparse token is inserted after the "enqueue_many" finishes. This
    # is needed to make sure enough sparse gradients have been enqueued
    # before applying them to the variables.
    sparse_token_queue = (data_flow_ops.FIFOQueue(
        self._replicas_to_aggregate * 2,
        types_pb2.DT_INT32,
        shapes=(),
        shared_name="sparse_token_q_%s" % var.name))
    self._one_element_queue_list.append((sparse_token_queue, var.device))

    enqueue_spares_op = sparse_grad_queue.enqueue_many([grad.values,
                                                        grad.indices])
    with ops.control_dependencies([enqueue_spares_op]):
      train_ops.append(sparse_token_queue.enqueue((1,)))

    with ops.control_dependencies([sparse_token_queue.dequeue_many(
        self._replicas_to_aggregate)]):
      values, indices = sparse_grad_queue.dequeue_many(sparse_grad_queue.size())
      concat_grad = ops.IndexedSlices(values, indices, grad.dense_shape)

      # Sum the gradients of the same variables in the sparse layers so
      # that each variable is only updated once. Note that with 2
      # gradients g1 and g2 from 2 replicas for the same variable,
      # apply(g1+g2) is different from apply(g1) and then apply(g2) when
      # the optimizer is complex like Momentum or Adagrad.
      values = concat_grad.values
      indices = concat_grad.indices
      new_indices, indx = array_ops.unique(indices)
      num_indices = array_ops.shape(new_indices)[0]
      sum_values = math_ops.unsorted_segment_sum(values, indx, num_indices)
      return ops.IndexedSlices(sum_values, new_indices, concat_grad.dense_shape)