// Add a placeholder to the graph.
  auto placeholder_t =
      TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, status.get());
  TF_AbstractTensorGetEagerTensor(placeholder_t, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));

  TF_DeleteAbstractTensor(placeholder_t);
  TF_DeleteExecutionContext(graph_ctx);
}

    }
    case AttrValue::VALUE_NOT_SET:
      return builder->getUnitAttr();
    // kPlaceholder is not implemented.
    case AttrValue::kPlaceholder:
      return mlir::TF::PlaceholderAttr::get(builder->getContext(),
                                            value.placeholder());
    default:
      return tensorflow::errors::Unimplemented(
          absl::StrCat("Attribute ", value.DebugString()));
  }

  TensorShape x_shape({3, 5, 3});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  auto y = Slice(scope_, x, {1, 2, 1}, {1, 3, 2});
  RunTest(x, x_shape, y, {1, 3, 2});
}

TEST_F(ArrayGradTest, ConcatV2Grad) {
  TensorShape shape({3, 2, 5});
  std::vector<Output> xs;
  xs.push_back(Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)));
  xs.push_back(Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)));

  Scope scope = Scope::NewRootScope();
  TensorShape x_shape({1, 2, 3});
  std::vector<Output> xs;
  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape)));
  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape)));
  auto y = Stack(scope, xs, Stack::Axis(0));
  TensorShape y_shape({2, 1, 2, 3});
  double max_error;

  Output y = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(y_shape));
  auto z = Einsum(scope_, {x, y}, "i,i->");
  TensorShape z_shape({});
  RunTest({x, y}, {x_shape, y_shape}, {z}, {z_shape});
}

TEST_F(LinalgGradTest, Einsum_OuterProduct) {
  TensorShape x_shape({3});
  TensorShape y_shape({5});
  Output x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));

  TensorShape y_shape({1});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  auto y = L2Loss(scope_, x);
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(NNGradTest, BiasAddGradHelper) {
  TensorShape shape({4, 5});
  TensorShape bias_shape({5});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto bias = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(bias_shape));

  auto x2 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x2_shape));
  auto y = Add(scope_, x1, x2);
  RunTest({x1, x2}, {x1_shape, x2_shape}, {y}, {x1_shape});
}

TEST_F(NaryGradTest, AddV2) {
  TensorShape x1_shape({3, 2, 5});
  TensorShape x2_shape({2, 5});
  auto x1 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x1_shape));

    placeholder: e.g., Linux Ubuntu 16.04 validations: required: false

-   type: input id: Python attributes: label: Python version description:
    placeholder: e.g., 3.9 validations: required: false

-   type: input id: Bazel attributes: label: Bazel version description: if
    compiling from source placeholder: validations: required: false

  Output input = ops::Placeholder(root.WithOpName("input"), DT_FLOAT);
  Output begin = ops::Placeholder(root.WithOpName("begin"), DT_INT64);

  // The C++ node bindings immediately error out when we try construct a bogus
  // slice so we first use a placeholder to construct the Slice and then replace
  // the input.
  Output size_placeholder = ops::Placeholder(root.WithOpName("size"), DT_INT64);
  Output slice =

   *                 v   v
   */
  // Define
  TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");
  TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");
  Define(-1, {}, {feed1, feed2}, {feed2, feed1}, {});

  // Use, run, and verify
  TF_Operation* two = ScalarConst(2, host_graph_, s_);
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({two, func_feed});