}

@test "API compatibility test passes, ensuring no unexpected changes to the TF API" {
    bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test
    echo "You have to re-generate the TF API goldens and have the API changes reviewed."
    echo "Look at the instructions for ':api_compatibility_test -- --update_goldens=True'"
}

# See b/279852433 (internal).

if [[ -z "${TFCI:-}" ]]; then
  echo '==TFCI==: The $TFCI variable is not set. This is fine as long as you'
  echo 'already sourced a TFCI env file with "set -a; source <path>; set +a".'
  echo 'If you have not, you will see a lot of undefined variable errors.'
else
  FROM_ENV=$(mktemp)
  # "export -p" prints a list of environment values in a safe-to-source format,
  # e.g. `declare -x TFCI_BAZEL_COMMON_ARGS="list of args"` for bash.

config_setting(
    name = "fuchsia",
    constraint_values = if_google(
        ["//third_party/bazel_platforms/os:fuchsia"],
        [],
    ),
    values = if_oss(
        # TODO(b/149248802) When we have a Fuchsia Bazel SDK update to use the values it sets.
        {"cpu": "fuchsia"},
        {},
    ),
    visibility = ["//visibility:public"],
)

config_setting(
    name = "fuchsia_x86_64",

# TODO: also enable this flag after fixing the visibility violations
# build --incompatible_config_setting_private_default_visibility

# Default options should come above this line.

# Android configs. Bazel needs to have --cpu and --fat_apk_cpu both set to the
# target CPU to build transient dependencies correctly. See
# https://docs.bazel.build/versions/master/user-manual.html#flag--fat_apk_cpu

  # Check if current bazel version can be detected properly.
  if not curr_version_int:
    print('WARNING: current bazel installation is not a release version.')
    return curr_version

  print('You have bazel %s installed.' % curr_version)
  return curr_version


def set_cc_opt_flags(environ_cp):
  """Set up architecture-dependent optimization flags.

  Also append CC optimization flags to bazel.rc..

        // artificial restriction and require that when num_opers=-1, such
        // nodes must have a single output.
        if (node->num_outputs() != 1) {
          return InvalidArgument(
              "When `num_opers` is set to -1, nodes referenced in `inputs` "
              "must have a single output. Node ",
              node->name(), " has ", node->num_outputs(),

  }

  const tensorflow::OpDef_ArgDef& input_arg = op_def->input_arg()[input_index];

  if (input_arg.number_attr().empty()) {
    status->status = tensorflow::errors::NotFound(
        op_name, " does not have number_attr() defined.");
    return nullptr;
  }

  // The returned string is owned by OpRegistry, so liveness is not a concern.
  return input_arg.number_attr().c_str();
}

  TFE_TensorHandle* h1 = TestVariable(ctx, 2.0, task2_name);
  TFE_TensorHandle* h2 = TestVariable(ctx, 3.0, task0_name);

  // Add a sync point to make sure that variables have been initialized
  // before the function execution starts.
  TFE_ContextAsyncWait(ctx, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Pack 3 variable handles into one TFE_TensorHandle.