bool overlapping;
  TF_RETURN_IF_ERROR(
      GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping));
  auto dx = internal::FractionalAvgPoolGrad(
      scope, Shape(scope, op.input(0), Shape::OutType(DT_INT64)),
      grad_inputs[0], op.output(1), op.output(2),
      internal::FractionalAvgPoolGrad::Overlapping(overlapping));
  grad_outputs->push_back(dx);
  return scope.status();
}

  // so it's fine to convert it back to int32 regardless of truncation.
  auto params = op.input(0);
  auto colocate_scope = scope.ColocateWith(params);
  Shape::Attrs shape_attrs;
  shape_attrs.out_type_ = DT_INT64;
  auto params_shape64 = Shape(colocate_scope, params, shape_attrs);
  Output params_shape = Cast(colocate_scope, params_shape64, DT_INT32);

  auto indices = op.input(1);

  ReplaceAllUsesWithCast(concat.getValue(), buffer);

  // Create the lengths as a list of the same value (element size).
  tensorflow::Tensor lengths_tensor(tensorflow::DT_INT64,
                                    {buffer_type.getDimSize(0)});
  for (int64_t i = 0; i < buffer_type.getDimSize(0); ++i) {
    lengths_tensor.vec<int64_t>()(i) = buffer_type.getDimSize(1);
  }

  ASSERT_TRUE(name_and_attrs.ParseFromArray(serialized_attr_values->data,
                                            serialized_attr_values->length));
  ASSERT_EQ("VarHandleOp", name_and_attrs.name());
  ASSERT_EQ(tensorflow::DT_INT64,
            name_and_attrs.attr().find("dtype")->second.type());
  TF_DeleteBuffer(serialized_attr_values);

  TFE_Op* var_op_2 = TFE_NewOp(ctx, "VarHandleOp", status);

  string serialized_dtype;

  EXPECT_EQ(TF_COMPLEX64, static_cast<TF_DataType>(tensorflow::DT_COMPLEX64));
  EXPECT_EQ(TF_COMPLEX, TF_COMPLEX64);
  EXPECT_EQ(TF_INT64, static_cast<TF_DataType>(tensorflow::DT_INT64));
  EXPECT_EQ(TF_BOOL, static_cast<TF_DataType>(tensorflow::DT_BOOL));
  EXPECT_EQ(TF_QINT8, static_cast<TF_DataType>(tensorflow::DT_QINT8));
  EXPECT_EQ(TF_QUINT8, static_cast<TF_DataType>(tensorflow::DT_QUINT8));

    if (status.ok()) {
      // We can only leave the transpose op on host if its dtype is supported on
      // host.
      if (dtype == tensorflow::DT_UINT64 || dtype == tensorflow::DT_INT64 ||
          dtype == tensorflow::DT_UINT32 || dtype == tensorflow::DT_INT32 ||
          dtype == tensorflow::DT_UINT16 || dtype == tensorflow::DT_INT16 ||