std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_Context* eager_ctx =
      TF_ExecutionContextGetTFEContext(wrap(ctx), status.get());
  TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
  TFE_TensorHandle* input_eager =
      TestTensorHandleWithDims<T, datatype>(eager_ctx, data, dims, num_dims);
  *tensor =

    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  AbstractTensorHandle* loss;
  AbstractTensorHandle* backprop;
  TF_RETURN_IF_ERROR(ops::SparseSoftmaxCrossEntropyWithLogits(
      ctx, inputs[0], inputs[1], &loss, &backprop,
      "SparseSoftmaxCrossEntropyWithLogits"));
  // `gradient_checker` only works with model that returns only 1 tensor.

# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early
if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then
  cat <<EOF
ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable.
       TFCI: $TFCI
       TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX is "$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX".

  EXPECT_EQ(TF_ALREADY_EXISTS,
            static_cast<TF_Code>(tensorflow::error::ALREADY_EXISTS));
  EXPECT_EQ(TF_PERMISSION_DENIED,
            static_cast<TF_Code>(tensorflow::error::PERMISSION_DENIED));
  EXPECT_EQ(TF_UNAUTHENTICATED,
            static_cast<TF_Code>(tensorflow::error::UNAUTHENTICATED));
  EXPECT_EQ(TF_RESOURCE_EXHAUSTED,

  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);
}
TEST(CAPI, Execute_MatMul_CPU_Runtime_Error) {
  Execute_MatMul_CPU_Runtime_Error(false);
}
TEST(CAPI, Execute_MatMul_CPU_Runtime_ErrorAsync) {
  Execute_MatMul_CPU_Runtime_Error(true);
}

void Execute_MatMul_CPU_Type_Error(bool async) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();

  // Step 1: Load plugin
  Env* env = Env::Default();
  void* dso_handle;
  TF_RETURN_IF_ERROR(env->LoadDynamicLibrary(dso_path.c_str(), &dso_handle));

  // Step 2: Load symbol for `TF_InitPlugin`
  void* dso_symbol;
  TF_RETURN_WITH_CONTEXT_IF_ERROR(
      env->GetSymbolFromLibrary(dso_handle, "TF_InitPlugin", &dso_symbol),
      "Failed to load TF_InitPlugin symbol for DSO: ", dso_path);

  ImmediateTensorHandlePtr dim(imm_ctx->CreateLocalHandle(minus_1.get()));
  AbstractTensorHandle* expand_dims_outputs;
  TF_RETURN_IF_ERROR(
      ops::ExpandDims(ctx, vec, dim.get(), &expand_dims_outputs, "ExpandDims"));
  TF_RETURN_IF_ERROR(
      ops::Mul(ctx, expand_dims_outputs, mat, &outputs[0], "Mul"));
  expand_dims_outputs->Unref();
  return absl::OkStatus();
}

  AbstractOperationPtr op(ctx->CreateOperation());
  TF_RETURN_IF_ERROR(op->Reset("ZerosLike", /*raw_device_name=*/nullptr));
  if (isa<tracing::TracingOperation>(op.get())) {
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingOperation>(op.get())->SetOpName(
        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;

    shape_string = shape.DebugString();
  } else {
    shape_string = "<error computing shape>";
  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }

  OwnedTFStatus c_status(TF_NewStatus());
  init_fn(&params, c_status.get());
  TF_RETURN_IF_ERROR(tsl::StatusFromTF_Status(c_status.get()));
  TF_RETURN_IF_ERROR(ValidateTPOptimizerRegistrationParams(params));
  TF_RETURN_IF_ERROR(ValidateTPOptimizer(optimizer));
  TF_RETURN_IF_ERROR(ValidateTPOptimizerConfigs(optimizer_configs));

  CGraphOptimizerRegister(