}

  dlm_tensor->dl_tensor.shape = shape_arr->data();
  // There are two ways to represent compact row-major data
  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.

else:
  _module_dir = _module_util.get_parent_dir_for_name("keras.api._v2.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__


# Enable TF2 behaviors
from tensorflow.python.compat import v2_compat as _compat
_compat.enable_v2_behavior()
_major_api_version = 2


# Load all plugin libraries from site-packages/tensorflow-plugins if we are
# running under pip.

elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.
# Import compat before trying to import summary from tensorboard, so that
# reexport_tf_summary can get compat from sys.modules. Only needed if using
# lazy loading.
_current_module.compat.v2  # pylint: disable=pointless-statement

# Load tensorflow-io-gcs-filesystem if enabled

+# define __attribute_nonstring__ __attribute__ ((__nonstring__))
+#else
+# define __attribute_nonstring__
+#endif
+
 #if defined __LONG_DOUBLE_MATH_OPTIONAL && defined __NO_LONG_DOUBLE_MATH
 # define __LDBL_COMPAT 1
 # ifdef __REDIRECT
diff --git a/nis/nss_nisplus/nisplus-parser.c b/nis/nss_nisplus/nisplus-parser.c
index a4d76fb..41600f0 100644
--- a/nis/nss_nisplus/nisplus-parser.c
+++ b/nis/nss_nisplus/nisplus-parser.c

TENSORFLOW_API_INIT_FILES_V2 = (
    TENSORFLOW_API_INIT_FILES +
    get_compat_files(TENSORFLOW_API_INIT_FILES, 2) +
    get_compat_files(TENSORFLOW_API_INIT_FILES_V1, 1) + get_nested_compat_files([
        1,
        2,
    ])
)

TENSORFLOW_API_INIT_FILES_V1 = (
    TENSORFLOW_API_INIT_FILES_V1 +
    get_compat_files(TENSORFLOW_API_INIT_FILES, 2) +
    get_compat_files(TENSORFLOW_API_INIT_FILES_V1, 1) + get_nested_compat_files([

inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the

    `compat.v2` module. It contains a copy of the 1.15 main module (without
    `contrib`) in the `compat.v1` module. TensorFlow 1.15 is able to emulate 2.0
    behavior using the `enable_v2_behavior()` function. This enables writing
    forward compatible code: by explicitly importing either
    `tensorflow.compat.v1` or `tensorflow.compat.v2`, you can ensure that your