// need for `Env::Default()`. At that time, we might decide to remove the need
// for `Env::Default()` altogether, but that's a different project, not in
// scope for now. I'm just mentioning this here as that transition will mean
// removal of the registration part from `Env` and adding it here instead: we
// will need tables to hold for each scheme the function tables that implement

    // If this file does not exist on server, we will need to sync it.
    bool sync_need = (status_code == TF_NOT_FOUND);
    file->plugin_file = new tf_writable_file::GCSFile(
        {std::move(bucket), std::move(object), &gcs_file->gcs_client,
         TempFile(temp_file_name, std::ios::binary | std::ios::app), sync_need,
         -1});
  } else {
    // If compose is true, we do not download anything.

// ---------------------  Coordination service  --------------------------------
// Returns a not owning pointer to the coordination service agent, which is
// opaque to plugin. Plugin OpKernels need to use the accompanying C APIs to
// access coordination service functionalities.
TF_CAPI_EXPORT extern TF_CoordinationServiceAgent*
TF_GetCoordinationServiceAgent(TF_OpKernelContext* ctx);

                                                           TF_DeleteStatus);

  TF_Operation* feed = Placeholder(func_graph.get(), s.get());
  TF_Operation* neg = Neg(feed, func_graph.get(), s.get());

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,

        *   The random-number-generating ops in the `tf.random` module when the
            global random seed has not yet been set (via `tf.random.set_seed`).
            Throws `RuntimeError` from Python or `InvalidArgument` from C++
        *   `tf.compat.v1.get_seed` if the global random seed has not yet been
            set (via `tf.random.set_seed`). Throws `RuntimeError` from Python or
            `InvalidArgument` from C++

## Security

# limitations under the License.
# ==============================================================================
#
# Usage: rename_and_verify_wheels.sh
# This script is aware of TFCI_ variables, so it doesn't need any arguments.
# Puts new wheel through auditwheel to rename and verify it, deletes the old
# one, checks the filesize, and then ensures the new wheel is installable.
set -euxo pipefail

cd "$TFCI_OUTPUT_DIR"

// gradients.
//
// `ZerosLike` is not expected to be called and returns a nullptr. The creation
// of default zeros grads is handled by the `DefaultGradientFunction` registered
// for each op.
// TODO(srbs): We need to define `ZerosLike` here to keep the compiler happy.
// Figure out a way to avoid this.
// TODO(srbs): Should ZerosLike check-fail instead of returning nullptr?
class TapeTensor {
 public:

/// `.cc` file is only used by core TensorFlow to implement checking needed for
/// plugin registration and ensuring API and ABI compatibility. Plugin authors
/// don't need to read the `.cc` file but they should consult every section of
/// this file to ensure a compliant plugin can be built and that the plugin can
/// be used without recompilation in the widest range of TensorFlow versions.
///

TF_Tensor* Int32Tensor(int32_t v);

TF_Tensor* DoubleTensor(double v);

TF_Tensor* FloatTensor(float v);

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,
                          const std::vector<int64_t>& dims = {});

TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,

computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are