for (int i = 0; i < num_inputs; ++i) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, i, status);
    if (TF_GetCode(status) != TF_OK) return;
    const char* tensor_handle_device =
        TFE_TensorHandleDeviceName(input, status);
    if (TF_GetCode(status) != TF_OK) return;
    if (named_device->name() == tensor_handle_device) {
      // We assume that any tensors already placed on this device are

    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing input ", i, " into function '", fn_name,
        "'");
    TF_RETURN_WITH_CONTEXT_IF_ERROR(ValidateNonRefOutput(node, idx),
                                    "Encountered while processing input ", i,

  auto* cc_ctx = reinterpret_cast<tensorflow::OpKernelContext*>(ctx);
  absl::Status cc_status;
  if (var_info == nullptr) {
    cc_status = absl::InvalidArgumentError("TF_VariableInfo is NULL.");
    status->status = cc_status;
    return;
  }
  if (var_info->var_info.var() == nullptr) {
    cc_status = absl::InvalidArgumentError(
        "VariableInfo does not track a resource variable.");

        TFE_DeleteOp);
    if (TF_GetCode(status.get()) != TF_OK) return;
    TFE_OpSetAttrInt(op.get(), "num_replicas", 1);
    TFE_OpAddInput(op.get(), value_one.get(), status.get());
    if (TF_GetCode(status.get()) != TF_OK) return;
    TFE_OpSetDevice(op.get(), device_name, status.get());
    if (TF_GetCode(status.get()) != TF_OK) return;

    TFE_TensorHandle* result_handles;

 public:
  static bool enabled_;
  GraphErrorInjectionPass() {}

  tensorflow::Status Run(
      const tensorflow::GraphOptimizationPassOptions& options) override {
    if (!enabled_) {
      return absl::OkStatus();
    }
    if (first_call_) {
      first_call_ = false;
      return absl::OkStatus();
    }
    return tensorflow::errors::Internal("Graph pass runs for more than once!");
  }

 private:

namespace tensorflow {
namespace {

using ::testing::HasSubstr;
using ::testing::NotNull;
using ::tsl::testing::StatusIs;

PJRT_Buffer* CreateCBuffer() {
  auto status = pjrt::PjrtApi(DEVICE_CPU);
  if (!status.ok()) {
    CHECK_OK(pjrt::SetPjrtApi(DEVICE_CPU, GetPjrtApi()));
  }
  auto pjrt_client = xla::GetCApiClient(DEVICE_CPU);
  CHECK_OK(pjrt_client.status());

    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

Status RegisterGradients(GradientRegistry* registry) {
  TF_RETURN_IF_ERROR(RegisterNotDifferentiable(registry, "CheckNumerics"));
  return absl::OkStatus();
}

TEST_P(CppGradients, TestSetAttrString) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(

#include "tensorflow/c/eager/abstract_tensor_handle.h"

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {
    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,

  TF_InferStatically(graph_properties, true, false, false, false, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  for (const NodeDef& node : item.graph.node()) {
    if (node.op() == "AddN") {
      int num_values = 0;
      TF_GetInputPropertiesListSize(graph_properties, node.name().c_str(),
                                    &num_values, status);

                                TF_RANDOM_ACCESS_FILE_OPS_ABI,
                                "random access file"));

  if (ops->writable_file_ops != nullptr)
    TF_RETURN_IF_ERROR(CheckABI(ops->writable_file_ops_abi,
                                TF_WRITABLE_FILE_OPS_ABI, "writable file"));

  if (ops->read_only_memory_region_ops != nullptr)