_kernel_dir = _os.path.join(_tf_dir, "core", "kernels")
  if _os.path.exists(_kernel_dir):
    _ll.load_library(_kernel_dir)

  # Load third party dynamic kernels.
  for _s in _site_packages_dirs:
    _plugin_dir = _os.path.join(_s, "tensorflow-plugins")
    if _os.path.exists(_plugin_dir):
      _ll.load_library(_plugin_dir)
      # Load Pluggable Device Library

  _kernel_dir = _os.path.join(_tf_dir, "core", "kernels")
  if _os.path.exists(_kernel_dir):
    _ll.load_library(_kernel_dir)

  # Load third party dynamic kernels.
  for _s in _site_packages_dirs:
    _plugin_dir = _os.path.join(_s, "tensorflow-plugins")
    if _os.path.exists(_plugin_dir):
      _ll.load_library(_plugin_dir)
      # Load Pluggable Device Library

  if (ops_->path_exists == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(
        "Filesystem for ", fname, " does not support FileExists()"));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  const std::string translated_name = TranslateName(fname);
  ops_->path_exists(filesystem_.get(), translated_name.c_str(),

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }
  registry_.insert({op_name, gradient_function_factory});
  return absl::OkStatus();
}
Status GradientRegistry::Lookup(

            static_cast<TF_Code>(tensorflow::error::DEADLINE_EXCEEDED));
  EXPECT_EQ(TF_NOT_FOUND, static_cast<TF_Code>(tensorflow::error::NOT_FOUND));
  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,

your changes by running the command:

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

    int64_t init_timeout_in_ms, int retries, TF_Status* status,
    bool clear_existing_contexts);

// Checks whether a remote worker is alive or not. This will return true even if
// the context doesn't exist on the remote worker.
TF_CAPI_EXPORT extern bool TFE_ContextCheckAlive(TFE_Context* ctx,
                                                 const char* worker_name,
                                                 TF_Status* status);

  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* n = TestMatrixTensorHandle100x100(ctx);
  // If a GPU exists, copy the handle to GPU so that we can exercise
  // unprotecting a mirror.
  std::string gpu_device_name;
  if (GetDeviceName(ctx, &gpu_device_name, "GPU")) {
    TFE_TensorHandle* n_gpu =

typedef struct TF_DeviceList TF_DeviceList;

// Lists all devices in a TF_Session.
//
// Caller takes ownership of the returned TF_DeviceList* which must eventually
// be freed with a call to TF_DeleteDeviceList.
TF_CAPI_EXPORT extern TF_DeviceList* TF_SessionListDevices(TF_Session* session,
                                                           TF_Status* status);

// Lists all devices in a TF_Session.
//

       TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX is "$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX".
       But in .bazelrc, there is no "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX".
       That setting must exist for this script to function correctly.
EOF
  exit 1
fi
TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX"

# These flags share the user's gcloud credentials with the container, so that bazel