return absl::OkStatus();
  }
  Status SetAttrShapeList(const char* attr_name, const int64_t** dims,
                          const int* num_dims, int num_values) override {
    std::vector<PartialTensorShape> shapes;
    shapes.reserve(num_values);
    for (int i = 0; i < num_values; ++i) {
      if (num_dims[i] < 0) {
        shapes.emplace_back();
      } else {
        shapes.emplace_back(ArraySlice<int64_t>(

}

TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype,
                                  const int64_t* dims, int num_dims,
                                  TF_Status* status) {
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  if (ctx == nullptr) {

TEST(CAPI_EXPERIMENTAL, IsStateful) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  int assign = TF_OpIsStateful("AssignAddVariableOp", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(assign, 1);
  int id = TF_OpIsStateful("Identity", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(id, 0);
}

  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,
      MemoryReleaser memory_releaser, void* memory_releaser_arg) = 0;

  // Create a handle to wrap and manage a Tensor
  virtual ImmediateExecutionTensorHandle* CreateLocalHandle(

//
TF_CAPI_EXPORT extern TF_ShapeAndTypeList* TF_NewShapeAndTypeList(
    int num_shapes);
TF_CAPI_EXPORT extern void TF_ShapeAndTypeListSetShape(
    TF_ShapeAndTypeList* shape_list, int index, const int64_t* dims,
    int num_dims);
TF_CAPI_EXPORT extern void TF_ShapeAndTypeListSetUnknownShape(
    TF_ShapeAndTypeList* shape_list, int index);
TF_CAPI_EXPORT extern void TF_ShapeAndTypeListSetDtype(

-{
-  size_t __result = 0;
-  while (__s[__result] != '\0' && __s[__result] != __reject1
-	 && __s[__result] != __reject2)
-    ++__result;
-  return __result;
-}
-
-__STRING_INLINE size_t __strcspn_c3 (const char *__s, int __reject1,
-				     int __reject2, int __reject3);
-__STRING_INLINE size_t
-__strcspn_c3 (const char *__s, int __reject1, int __reject2,
-	      int __reject3)
-{

  }

  {
    std::unique_ptr<ParallelTensor> int_tensors =
        parallel_device.ScalarsFromSequence<int>({5, 6}, context.get(),
                                                 status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    ExpectScalarEq<int>(int_tensors->tensor(0), 5);
    ExpectScalarEq<int>(int_tensors->tensor(1), 6);
  }
}

}  // namespace parallel_device

// this by not constructing zeros to pass for those indices.
std::unordered_map<string, std::unordered_set<int>>*
FunctionsAcceptingNoneForIndicesMap() {
  static auto* const m =
      new std::unordered_map<string, std::unordered_set<int>>({
          {"SoftmaxCrossEntropyWithLogits", {1}},
          {"SparseSoftmaxCrossEntropyWithLogits", {1}},
          {"FusedBatchNorm", {1, 2, 3, 4}},

#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

namespace {

using ::tensorflow::string;

void ReplaceTaskInServerDef(tensorflow::ServerDef* server_def, int task_index) {
  tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0);
  int port = tensorflow::testing::PickUnusedPortOrDie();
  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat("localhost:", port);
}

                         {12, 15, 18, 12, 15, 18, 12, 15, 18},
                         {6, 6, 6, 15, 15, 15, 24, 24, 24},
                         {6, 15, 24, 6, 15, 24, 6, 15, 24}};

  for (int i{}; i < 4; ++i) {
    bool transpose_a = transpose_a_vals[i];
    bool transpose_b = transpose_b_vals[i];
    Model MatMulModel =
        [transpose_a, transpose_b](
            AbstractContext* ctx,