if (!s.ok()) {
      tsl::Set_TF_Status_from_Status(status, s);
      return;
    }
  }
}

void TF_GetOutputPropertiesList(TF_GraphProperties* graph_properties,
                                const char* name, TF_Buffer** properties,
                                int num_values, TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");

export TF_VER_SUFFIX=$(awk -F\" '/#define TF_VERSION_SUFFIX/ {print $2}' tensorflow/core/public/version.h)
export TF_VER_PYTHON=$(awk -F\' '/_VERSION =/ {print $2}' tensorflow/tools/pip_package/setup.py)

# Derived helper variables.
export TF_VER_SHORT="${TF_VER_MAJOR}.${TF_VER_MINOR}"

        # This is a mapping of name_container_map keys under sigbuild_tf_configs
        # to tag names on gcr.io/tensorflow-sigs/build.
        # TF 2.9
        map sigbuild-r2.9 2.9-python3.9
        map sigbuild-r2.9-python3.8 2.9-python3.8
        map sigbuild-r2.9-python3.9 2.9-python3.9
        map sigbuild-r2.9-python3.10 2.9-python3.10
        # TF 2.10
        map sigbuild-r2.10 2.10-python3.9

                                                     TF_Status* status);

// Get GraphDebugInfo containing stack traces mapping to node names
TF_CAPI_EXPORT extern void TFE_ContextGetGraphDebugInfo(
    TFE_Context* ctx, const char* function_name, TF_Buffer* buf,
    TF_Status* status);

// Extracts a TF_Function from the context.
// Must call TF_DeleteFunction on the returned value.

    }
    TF_Status* s = TF_NewStatus();
    auto* operation = TF_FinishOperation(tf_opdesc, s);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(s));
    TF_DeleteStatus(s);
    *num_retvals = TF_OperationNumOutputs(operation);
    for (int i = 0; i < *num_retvals; ++i) {
      retvals[i] = new GraphTensor({operation, i}, g_);
    }
    return absl::OkStatus();
  }

        "tf_tensor.h",
        "tf_tstring.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_internal",
        ":c_api_macros_hdrs",
        ":c_api_no_xla",
        ":tf_attrtype",
        ":tf_buffer",
        ":tf_file_statistics",
        ":tf_status_helper",
        ":tf_status_internal",
        ":tf_tensor_internal",
        ":tf_tstring",

        "//tensorflow/c/eager:gradients_internal",
    ],
)

tf_cuda_cc_test(
    name = "custom_gradient_test",
    size = "small",
    srcs = [
        "custom_gradient_test.cc",
    ],
    args = ["--heap_check="],  # TODO(b/174752220): Remove
    tags = tf_cuda_tests_tags(),
    deps = [
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c/eager:abstract_context",

  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 =
      TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =

* `tf.ones`, `tf.zeros`, `tf.fill`, `tf.ones_like`, `tf.zeros_like` now take an additional Layout argument that controls the output layout of their results.

  if (ctx == nullptr) {
    return;
  }

  // ctx->RefCountIsOne() should be true here.
  tensorflow::unwrap(ctx)->Release();
}

TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) {
  TF_DeviceList* l = new TF_DeviceList;
  tensorflow::unwrap(ctx)->ListDevices(&l->response);
  return l;
}

void TFE_ContextClearCaches(TFE_Context* ctx) {