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(

  status->status = c.construction_status();
  if (!status->status.ok()) return;

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);
  if (!status->status.ok()) return;

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);

#
# Usage: get_test_list.sh OUTPUT BAZEL_TEST_COMMAND...
# Writes the list of tests that would be run from BAZEL_TEST_COMMAND to OUTPUT.
# Hides all extra output and always exits with success for now.

OUTPUT=$1
shift

# one, checks the filesize, and then ensures the new wheel is installable.
set -euxo pipefail

cd "$TFCI_OUTPUT_DIR"

# Move extra wheel files somewhere out of the way. This script
# expects just one wheel file to exist.
if [[ "$(ls *.whl | wc -l | tr -d ' ')" != "1" ]]; then
  echo "More than one wheel file is present: moving the oldest to"
  echo "$TFCI_OUTPUT_DIR/extra_wheels."

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.
//

### Resource allocation

A denial of service caused by one model could bring down the entire server, but
we don't consider this as a vulnerability, given that models can exhaust
resources in many different ways and solutions exist to prevent this from
happening (e.g., rate limits, ACLs, monitors to restart broken servers).

### Model sharing

If the multitenant design allows sharing models, make sure that tenants and

*   `tf.cond` emits a StatelessIf op if the branch functions are stateless and
    do not touch any resources.
*   `tf.cond`, `tf.while` and `if` and `while` in AutoGraph now accept a
    nonscalar predicate if has a single element. This does not affect non-V2
    control flow.
*   `tf.while_loop` emits a StatelessWhile op if the cond and body functions are

namespace {

template <typename BackwardFunction, typename TapeTensor>
struct BackpropInitialState {
  OpTape<BackwardFunction, TapeTensor> op_tape;

  // Map from tensor ID to how many references still exist for this tensor in
  // the tape.
  std::unordered_map<int64_t, int64_t> tensor_usage_counts;

  // Maps from op ID to how many output tensors of this op still need to have
  // their gradients computed.