// 6. Create a context D using the full cluster server def E.
// 7. Read the variable in D.
TEST(CAPI, SingleHostServerDefV1Works) {
  // Create a server def that represents a 2-process cluster and a client.
  // Example:
  //
  // cluster { job { name: "worker"
  //                 tasks { key: 0 value: "localhost:14522" }
  //                 tasks { key: 1 value: "localhost:14523" }
  //               }

// destructor. The easiest way to use MakeCleanup is with a lambda argument,
// capturing the return value in an 'auto' local variable. Most users will not
// need more sophisticated syntax than that.
//
// Example:
//   void func() {
//     FILE* fp = fopen("data.txt", "r");
//     if (fp == nullptr) return;
//     auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); });
//     // No matter what, fclose(fp) will happen.

//       versioning guarantees yet.
//   * `void* ext` is a free-form field that can be populated by
//     a plugin in `TP_*` structs or potential future extension points .
//
// Example usage:
//
//   /* Sample TensorFlow code below, exact implementation might differ. */
//   // Version checking uses `struct_size`. It should be set both by core
//   // and the plugin.
//   TP_OptimizerRegistrationParams params{

  // The value returned may be different from the one set by SetDeviceName, but
  // it will be compatible with it: the name will be updated by device placement
  // logic to refer to the specific device chosen.
  //
  // Example: If one calls `op->SetDeviceName("/device:GPU")`, the value
  // returned by DeviceName should be "/device:GPU:*" until a particular GPU is
  // chosen for the operation by the device placement logic in the

against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

# Here is a single-line example of running a script on Linux to build the
# GPU version of TensorFlow for Python 3.12, using the public TF bazel cache and
# a local build cache:
TFCI=py312,linux_x86_cuda,public_cache,disk_cache ci/official/wheel.sh

# iOS
*.pbxproj
*.xcworkspace
/*.podspec
/tensorflow/lite/**/coreml/**/BUILD
/tensorflow/lite/**/ios/BUILD
/tensorflow/lite/**/objc/BUILD
/tensorflow/lite/**/swift/BUILD
/tensorflow/lite/examples/ios/simple/data/*.tflite
/tensorflow/lite/examples/ios/simple/data/*.txt
Podfile.lock
Pods

//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test.py
//tensorflow/lite/python:interpreter
//tensorflow/lite/python:interpreter_test

namespace parallel_device {

// Allocate a parallel device named `device_name` which forwards operations to
// `underlying_devices`, maintaining "parallel tensors" with components placed
// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/job:localhost/replica:0/task:0/device:GPU:1"}

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the

// This function will block till the operation that produces `h` has
// completed. The memory returned might alias the internal memory used by
// TensorFlow. Hence, callers should not mutate this memory (for example by
// modifying the memory region pointed to by TF_TensorData() on the returned
// TF_Tensor).
TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,