//   Outputs:
  std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_);
  // TF_Status is an incomplete type and so can't be stack allocated. To avoid
  // unnecessary allocations each Execute call, we keep one heap-allocated
  // version for the thread.
  StatusPtr status_ TF_GUARDED_BY(execution_mutex_);

  const std::string device_;
  ExecutorPtr executor_ TF_GUARDED_BY(execution_mutex_);

  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);
  *reinterpret_cast<bool*>(arg) = true;
}

TEST(CAPI, Tensor) {
  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
          TF_TensorDefaultAlignment(), num_bytes));
  int64_t dims[] = {2, 3};

  for (int i = 0; i < len; ++i) {
    values[i] = TF_NewBuffer();
    status->status = MessageToBuffer(attr->list().shape(i), values[i]);
    if (!status->status.ok()) {
      // Delete everything allocated to far, the operation has failed.
      for (int j = 0; j <= i; ++j) {
        TF_DeleteBuffer(values[j]);
      }
      return;
    }
  }
}

TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually
only a placeholder to enable test cases to run. The TensorFlow build replaces

for an example.

See
[this commit](https://github.com/tensorflow/tensorflow/commit/5f7f05a80aac9b01325a78ec3fcff0dbedb1cc23)
as a rough example of the steps below.

All the files referenced below are located in the same directory as this README,
unless indicated otherwise.

1) Add the new version to the `VERSIONS` variable inside
   `tensorflow/tools/toolchains/python/python_repo.bzl`. \

TF_CAPI_EXPORT extern TF_Function* TFE_ContextGetFunction(TFE_Context* ctx,
                                                          const char* name,
                                                          TF_Status* status);

// Allocate and return a new Tensor on the host.
//
// The caller must set the Tensor values by writing them to the pointer returned
// by TF_TensorData with length TF_TensorByteSize.

  auto* rewrite_options =
      config->mutable_graph_options()->mutable_rewrite_options();
  rewrite_options->set_scoped_allocator_optimization(
      tensorflow::RewriterConfig::ON);
  rewrite_options->mutable_scoped_allocator_opts()->add_enable_op(
      "CollectiveReduce");

  if ((tensorflow::IsGoogleCudaEnabled() || tensorflow::IsBuiltWithROCm()) &&
      num_virtual_gpus > 0) {

        `tf.function`-local variable: during each execution of the
        `tf.function`, the variable will be created and then disposed, similar
        to a local (i.e. stack-allocated) variable in C/C++. Currently
        `experimental_enable_variable_lifting=False` only works on non-XLA
        devices (e.g. under `@tf.function(jit_compile=False)`).

*   TF SavedModel:

                "//tensorflow/lite/kernels/shim:tf_kernel_shim",
                "@local_xla//xla/stream_executor:stream_executor_impl",
                "@local_tsl//tsl/framework:bfc_allocator",
                "@local_tsl//tsl/framework:metrics",
            ] + tf_additional_binary_deps() +
            # TODO(b/259305727): Remove this select and include captured_function in macos builds.
            select({

}

void TF_InitPlugin(TF_FilesystemPluginInfo* info) {
  info->plugin_memory_allocate = plugin_memory_allocate;
  info->plugin_memory_free = plugin_memory_free;
  info->num_schemes = 2;
  info->ops = static_cast<TF_FilesystemPluginOps*>(
      plugin_memory_allocate(info->num_schemes * sizeof(info->ops[0])));
  ProvideFilesystemSupportFor(&info->ops[0], "");