for an example.

See
[this commit](https://github.com/tensorflow/tensorflow/commit/5f7f05a80aac9b01325a78ec3fcff0dbedb1cc23)
as a rough example of the steps below.

All the files referenced below are located in the same directory as this README,
unless indicated otherwise.

1) Add the new version to the `VERSIONS` variable inside
   `tensorflow/tools/toolchains/python/python_repo.bzl`. \

#      Ex. linux_x86_cuda   -- x86_64 Linux platform, with Nvidia CUDA support
#      Ex. macos_arm64      -- arm64 MacOS platform
# 3. Add modifiers. Some modifiers for local execution are:
#      Ex. disk_cache -- Use a local cache
#      Ex. public_cache -- Use TF's public cache (read-only)
#      Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only)

    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

    ```bash
    export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
    export flags="--config=opt --config=cuda -k"
    ```

    For example, to run all tests under tensorflow/python, do:

    ```bash

// EagerService to maintain consistency between client and worker. The
// context_id is initialized with a dummy value and is later set when the worker
// is initialized (either locally or remotely). The context_id can change during
// the process lifetime although this should cause the worker to be
// reinitialized (e.g. cleared caches) as well.
TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx);

[requirements updater readme](https://github.com/tensorflow/tensorflow/blob/master/ci/official/requirements_updater/README.md)

### Prerequisites for Local Testing

To run tests locally, follow these steps:

1.  Navigate to the relevant directory:
    ```
    cd ci/official/wheel_test
    ```
2.  Run a script for creating requirements file:
    ```

 	    {
 	      cp += 2 * nhere;
 	      continue;
diff --git a/locale/xlocale.h b/locale/xlocale.h
index 98c080b..843bd45 100644
--- a/locale/xlocale.h
+++ b/locale/xlocale.h
@@ -20,6 +20,9 @@
 #ifndef _XLOCALE_H
 #define _XLOCALE_H	1
 
+#ifndef _BITS_TYPES___LOCALE_T_H
+#define _BITS_TYPES___LOCALE_T_H 1
+
 /* Structure for reentrant locale using functions.  This is an

TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually
only a placeholder to enable test cases to run. The TensorFlow build replaces

# $TFCI_OUTPUT_DIR/script.log
exec > >(tee "$TFCI_OUTPUT_DIR/script.log") 2>&1

# Setup tfrun, a helper function for executing steps that can either be run
# locally or run under Docker. setup_docker.sh, below, redefines it as "docker
# exec".
# Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)".
# Therefore, "tfrun" commands cannot include pipes -- which is

// \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,

// Call once per control input to `desc`.
TF_CAPI_EXPORT extern void TF_AddControlInput(TF_OperationDescription* desc,
                                              TF_Operation* input);

// Request that `desc` be co-located on the device where `op`
// is placed.
//
// Use of this is discouraged since the implementation of device placement is
// subject to change. Primarily intended for internal libraries