const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }

#endif

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/kernels.h"

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);

                                 export_dir, tag_set, &bundle);
  if (!status->status.ok()) return nullptr;

  // Create a TF_Graph from the MetaGraphDef. This is safe as long as Session
  // extends using GraphDefs. The Graph instance is different, but equivalent
  // to the one used to create the session.
  //
  // TODO(jhseu): When Session is modified to take Graphs instead of

    test to timeout but it could be useful when you need to execute a single
    test or more in general your filtered/selected tests have a very low
    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash

TFE_MonitoringSampler0* TFE_MonitoringNewSampler0(
    const char* name, TFE_MonitoringBuckets* buckets, TF_Status* status,
    const char* description) {
  auto* result = new TFE_MonitoringSampler0(
      {name, buckets->create_buckets(), description});
  tsl::Set_TF_Status_from_Status(status, result->sampler->GetStatus());
  if (!result->sampler->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;
}

        optimizer.
    *   **Learning rate schedule access.** When using a `LearningRateSchedule`,
        The new optimizer's `learning_rate` property returns the
        current learning rate value instead of a `LearningRateSchedule` object
        as before. If you need to access the `LearningRateSchedule` object,
        please use `optimizer._learning_rate`.

# -------------------------------------------

# TensorFlow uses several libraries that may also be used by applications
# linking against the C and C++ APIs (such as libjpeg).  When we create
# the shared library, only export the core TF API functions to avoid
# causing library conflicts (e.g., those reported in github issue 1924).
# On Linux, tell the linker (-Wl,<option>) to use a version script that

        map sigbuild-r2.17-clang-python3.10 2.17-python3.10
        map sigbuild-r2.17-clang-python3.11 2.17-python3.11
        map sigbuild-r2.17-clang-python3.12 2.17-python3.12
    - name: Create Pull Request with changes
      uses: peter-evans/create-pull-request@2b011faafdcbc9ceb11414d64d0573f37c774b04 # v4.2.3
      with:
        title: Update the RBE images to the latest container versions

              'Android builds?'), 'Searching for NDK and SDK installations.',
             'Not configuring the WORKSPACE for Android builds.'):
    create_android_ndk_rule(environ_cp)
    create_android_sdk_rule(environ_cp)

  system_specific_test_config(environ_cp)

  configure_ios(environ_cp)

  print('Preconfigured Bazel build configs. You can use any of the below by '

      tf_posix_filesystem::NewAppendableFile;
  ops->filesystem_ops->new_read_only_memory_region_from_file =
      tf_posix_filesystem::NewReadOnlyMemoryRegionFromFile;
  ops->filesystem_ops->create_dir = tf_posix_filesystem::CreateDir;
  ops->filesystem_ops->delete_file = tf_posix_filesystem::DeleteFile;
  ops->filesystem_ops->delete_dir = tf_posix_filesystem::DeleteDir;