std::vector<std::string>* user_schemes = SchemeVector();
    if (!user_schemes->empty()) {
      auto is_requested_scheme = [user_schemes](const auto& scheme) {
        return std::find(user_schemes->begin(), user_schemes->end(), scheme) ==
               user_schemes->end();
      };
      auto end = std::remove_if(all_schemes->begin(), all_schemes->end(),
                                is_requested_scheme);

  while var is None:
    user_input_origin = get_input(question)
    user_input = user_input_origin.strip().lower()
    if user_input == 'y':
      print(yes_reply)
      var = True
    elif user_input == 'n':
      print(no_reply)
      var = False
    elif not user_input:
      if enabled_by_default:
        print(yes_reply)
        var = True

# Load rc file written by ./configure.
try-import %workspace%/.tf_configure.bazelrc
try-import %workspace%/xla_configure.bazelrc

# Load rc file with user-specific options.
try-import %workspace%/.bazelrc.user

# Here are bazelrc configs for release builds
# Build TensorFlow v2.
test:release_base --test_size_filters=small,medium

# Ensure release_base is set on linux

# (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C
# API) is responsible for registering ops with libtensorflow_framework.so. In
# addition to this core set of ops, user libraries which are loaded (via
# TF_LoadLibrary/tf.load_op_library) register their ops and kernels with this
# shared object directly.
#
# For example, from Python tf.load_op_library loads a custom op library (via