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], "");

  // Update the vector with information from `input_tensors` if provided.
  if (input_tensors != nullptr) {
    // Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {

        backward_function_getter, backward_function_deleter);
    return absl::OkStatus();
  }
  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
    return absl::OkStatus();
  }

  // We may need to allocate zero inputs for trainable dtypes we don't have JVPs
  // for. Make sure they get cleaned up.
  std::vector<Gradient*> new_zeros;
  auto delete_new_zeros = gtl::MakeCleanup([&new_zeros, this] {

  return new PJRT_Buffer{std::move(*buffer), c_api_client->pjrt_c_client()};
}

TEST(TensorPjRtBufferUtilTest, GetPjRtCBufferFromTensorNoBuffer) {
  auto allocator = std::make_unique<AsyncValueAllocator>();
  tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});

  EXPECT_THAT(
      GetPjRtCBufferFromTensor(&tensor),
      StatusIs(error::INTERNAL, HasSubstr(absl::StrCat(

  TF_VariableInfo() = delete;
  // TF_VariableInfo is constructed here by TensorFlow, and will be passed to
  // plugin as a opaque pointer. Plugin will need to call C APIs below to
  // operate on TF_VariableInfo (such as allocate temp tensor for the `var` held
  // by the underlying tensorflow::VariableInfo.
  TF_VariableInfo(int index, const std::string& name, tensorflow::Var* var) {
    var_info = tensorflow::VariableInfo{index, name, var};
  }

// \brief Get a pointer to a symbol from a dynamic library.
//
// "handle" should be a pointer returned from a previous call to
// TF_LoadLibraryFromEnv. On success, place OK in status and return a pointer to
// the located symbol. Otherwise returns nullptr and set error status.
TF_CAPI_EXPORT extern void* TF_GetSymbolFromLibrary(void* handle,
                                                    const char* symbol_name,