==============================================================================*/

// MakeCleanup(f) returns an RAII cleanup object that calls 'f' in its
// 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");

                                  std::vector<Gradient*>*, bool)> {
 public:
  template <typename lambda_type>
  explicit ForwardFunction(lambda_type lambda)
      : std::function<Status(const std::vector<Gradient*>&,
                             std::vector<Gradient*>*, bool)>(lambda) {}
};

// Computes Jacobian-vector products using forward-mode automatic
// differentiation.
//

      ask_for_var=(
          'Please specify the (min) Android NDK API level to use. '
          '[Available levels: %s]'
      )
      % api_levels,
      check_success=(lambda *_: True),
      error_msg='Android-%s is not present in the NDK path.',
  )

  return android_ndk_api_level


def set_gcc_host_compiler_path(environ_cp):
  """Set GCC_HOST_COMPILER_PATH."""

  // Returns true if the cache is enabled. If false, the BlockFetcher callback
  // is always executed during Read.
  bool IsCacheEnabled() const { return block_size_ > 0 && max_bytes_ > 0; }

  // We can not pass a lambda with capture as a function pointer to
  // `TF_StartThread`, so we have to wrap `Prune` inside a static function.
  static void PruneThread(void* param) {
    auto ram_file_block_cache = static_cast<RamFileBlockCache*>(param);

    *   Raw TensorFlow functions can now be used in conjunction with the Keras
        Functional API during model creation. This obviates the need for users
        to create Lambda layers in most cases when using the Functional API.
        Like Lambda layers, TensorFlow functions that result in Variable
        creation or assign ops are not supported.
    *   Add support for passing list of lists to the `metrics` argument in Keras