namespace tensorflow {

class XlaDevice : public LocalDevice {
 public:
  // Given a tensor, sets `xla::Shape*` the shape of tensor's representation
  // on device, fully padded. On error, the contents of `xla::Shape*`
  // are undefined.
  typedef std::function<Status(const Tensor&, xla::Shape*)> PaddedShapeFn;

  // Wrapper class to store metadata about the XlaDevice, where it can be

 */

package org.gradle.api.internal.artifacts.ivyservice.resolveengine.excludes.factories

import groovy.json.JsonSlurper

class ExcludeJsonLogToCode {
    private final static List<String> WORDS = [
        "shake",
        "grain",
        "root",
        "rain",
        "cover",
        "crib",
        "lunchroom",
        "sort",
        "building",
        "fact",
        "grass",
        "planes",

  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Const(scope_, {{1}, {0}});
  auto y = GatherNd(scope_, x, indices);
  RunTest(x, shape, y, shape);
}

TEST_F(ArrayGradTest, GatherNdGrad_SliceIndexing_Int64) {
  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Cast(scope_, Const(scope_, {{1}, {0}}), DT_INT64);

  }
  func.return
}

// CHECK: %[[SHAPE:[0-9]*]] = "tf.Shape"(%[[ARG_0]])
// CHECK: %[[OP_A:[0-9]*]] = "tf.opA"(%[[SHAPE]])
// CHECK: %[[OP_B:[0-9]*]] = "tf.opB"(%[[SHAPE]], %[[OP_A]])
// CHECK: tf_device.replicate
// CHECK:   tf_device.return %[[SHAPE]], %[[OP_A]], %[[OP_B]]


// CHECK-LABEL: func @nested_ops

                       absl::Span<AbstractTensorHandle*> outputs) {
  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 0) {
    return errors::InvalidArgument(
        "Tensor expected to have scalar shape found rank: ", shape.dims());
  }
  return absl::OkStatus();
}

TEST_P(UnifiedAPI, TestTensorShapeScalar) {
  if (UseFunction() && UseMlir()) {

  if (pack_axis < 0) {
    pack_axis += rank;
  }

  // Concat out shape.
  for (int i = 0; i < rank; ++i) {
    int64_t dim_size = input_type.getDimSize(i);
    if (i == pack_axis) {
      dim_size *= count;
    }
    concat_out_shape.push_back(dim_size);
  }

  // Pack out shape.
  int j = 0;
  for (int i = 0; i < rank + 1; ++i) {
    if (i == pack_axis) {

  xs.push_back(Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)));
  auto y = AddN(scope_, xs);
  RunTest(xs, {shape, shape, shape}, {y}, {shape});
}

TEST_F(NaryGradTest, Add) {
  TensorShape x1_shape({3, 2, 5});
  TensorShape x2_shape({2, 5});
  auto x1 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x1_shape));
  auto x2 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x2_shape));

    TF_RETURN_IF_ERROR(session->Create(graph));
    RunOptions run_options;
    Tensor input_a = CreateInputTensor(shape, 0);
    Tensor input_b = CreateInputTensor(shape, shape.num_elements());
    Tensor input_c = CreateInputTensor(shape, 2 * shape.num_elements());
    TF_RETURN_IF_ERROR(session->Run(
        run_options,
        {std::make_pair("a", input_a), std::make_pair("b", input_b),

   node [fontname = "Sans"];
   edge [fontname = "Sans"];

   "apiElements" [shape="box", label=<<B>apiElements</B>(C)>, color="pink", bgcolor="pink", style="filled"]
   "apiElements" -> "api"
   "apiElements" -> "compileOnlyApi"

   "api" [shape="box", label=<<B>api</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]
   "compileOnlyApi" [shape="box", label=<<B>compileOnlyApi</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]

// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 0 ],
// CHECK-NEXT:       name: "input_to_input_intermediate",
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 0 ],
// CHECK-NEXT:       name: "input_to_forget_intermediate",
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 0 ],
// CHECK-NEXT:       name: "input_to_cell_intermediate",