StringRef getArgument() const final {
    return "quant-duplicate-shape-determining-constants";
  }

  StringRef getDescription() const final {
    return "Duplicates shape-determining constants. A shape-determining "
           "constant is a constant that are transitively used to change or "
           "determine the shape of a tensor. For example, the second argument "

    EXPECT_LT(max_error, 1e-4);
  }

  Scope scope_;
};

TEST_F(ManipGradTest, RollGrad) {
  TensorShape shape({5, 4, 3});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Roll(scope_, x, {2, 1}, {0, 1});
  RunTest(x, shape, y, shape);
}

}  // namespace

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs;
  const std::vector<int64_t>* shape;
  Status s = handles[0]->Shape(&shape);
  ASSERT_TRUE(s.ok());
  EXPECT_EQ(0, shape->size());
}

TEST(PARALLEL_DEVICE_LIB, TestCancelOnError) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

    %VarHandleOp, %ctl = VarHandleOp device("/CPU:0") name("x") {_output_shapes = [#tf_type.shape<>], allowed_devices = [], container = "a", dtype = i64, shape = #tf_type.shape<>, shared_name = "x"} : () -> (tensor<!tf_type.resource<tensor<i64>>>)

                this.canon = "/" + this.share + "/" + StringUtil.join("/", remainParts) + ( trailingSlash ? "/" : "" );
            }
            else {
                this.unc = "\\";
                if ( this.share != null ) {
                    this.canon = "/" + this.share + ( trailingSlash ? "/" : "" );
                }
                else {

// 1. does not cause an error
// 2. preserves dtype and shape.
TEST_P(SavedVariableLoadingTest, LoadSavedVariableSuccessful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;
  saved_variable.set_dtype(dtype);
  shape.AsProto(saved_variable.mutable_shape());

  std::unique_ptr<Variable> var;

// CHECK:        subgraphs: [ {
// CHECK-NEXT:     tensors: [ {
// CHECK-NEXT:       shape: [ 3, 2 ],
// CHECK-NEXT:       buffer: 1,
// CHECK-NEXT:       name: "serving_default_x",
// CHECK-NEXT:       quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 3, 2 ],
// CHECK-NEXT:       buffer: 2,
// CHECK-NEXT:       name: "tfl.pseudo_const",

      std::vector<int64_t> shape;
      shape.push_back(static_cast<int64_t>(data.size()));
      attributes.emplace_back(builder.getNamedAttr(
          "window_strides", BuildVhloTensorV1Attr(shape, data, builder)));
    }
    if (!(op->padding.empty())) {
      std::vector<int64_t> shape;
      shape.push_back(static_cast<int64_t>(op->padding.size()) / 2);
      shape.push_back(2);

	{`c:/`, `c:/`, ``},
	{`c:/foo`, `c:/`, `foo`},
	{`c:/foo/bar`, `c:/foo/`, `bar`},
	{`//host/share`, `//host/share`, ``},
	{`//host/share/`, `//host/share/`, ``},
	{`//host/share/foo`, `//host/share/`, `foo`},
	{`\\host\share`, `\\host\share`, ``},
	{`\\host\share\`, `\\host\share\`, ``},
	{`\\host\share\foo`, `\\host\share\`, `foo`},
}

func TestSplit(t *testing.T) {
	var splittests []SplitTest

            tensor_spec.TensorSpec(
                shape=shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
    )
    return model

  # Prepares sample einsum input data shapes.
  # This function returns:
  # 1. Shape for input 1
  # 2. Shape for input 2
  # 3. Shape for bias
  # 4. Signature for input 1 (Could contain None dimension)