```

TensorFlow kernels and TensorFlow's dependencies are still not built with
debugging information with `--config=dbg`, as issues occur on Linux if
there is too much debug info (see [this GitHub
issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If
you want to debug a kernel, you can compile specific files with `-g` using the

## Wheel Test

This directory is dedicated to tests that require a built TensorFlow wheel
file for testing, such as:

* Ensuring the entire API is importable
* Testing downstream projects against the wheel

Ensure you have Bazel installed and accessible from your command line.

These tests use hermetic Python. They also require a built TensorFlow wheel file
and a requirements_lock file. The requirements_lock file is generated by the

# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early
if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then
  cat <<EOF
ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable.
       TFCI: $TFCI

export TF_VER_PATCH=$(awk '/#define TF_PATCH_VERSION/ {print $3}' tensorflow/core/public/version.h)

# Note: in awk, "print $N" prints the Nth "field", where fields are strings separated
# by whitespace. The flag "-F<x>" changes the behavior such that fields are now strings
# separated by the character <x>. Therefore, -F\' (escaped single quote) means that field
# $2 in <Tensor'flow'> is <flow>. This is useful for reading string literals like below.

# specifically. Use your best judgment to keep the scripts in this directory
# lean and easy to follow. When in doubt, remember that for CI scripts, "keep it
# simple" is MUCH more important than "don't repeat yourself."

# -e: abort script if one command fails
# -u: error if undefined variable used
# -x: log all commands
# -o pipefail: entire command fails if pipe fails. watch out for yes | ...

// parsed.
//
// Furthermore, as `INSTANTIATE_TEST_SUITE_P` needs to be at global level and we
// don't want to have a `std::vector<std::string>` at global level, we use a
// static pointer to such a vector: we construct it via `SchemeVector()` below
// and when tests are instantiated we process it using `GetSchemes()`.
static std::vector<std::string>* SchemeVector() {

FROM mcr.microsoft.com/dotnet/framework/sdk:4.8-windowsservercore-ltsc2019

# Set default powershell policy for this script (ProgressPreference='SilentlyContinue' makes
# downloads with Invoke-WebRequest not show the progress bar and is MUCH faster).
SHELL ["powershell.exe", "-ExecutionPolicy", "Bypass", "-Command", "$ErrorActionPreference='Stop'; $ProgressPreference='SilentlyContinue'; $VerbosePreference = 'Continue';"]

#   your builds. This usually saves a lot of time, especially when
#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures
#      or tests passing incorrectly.
#    - Automatic LLVM updates are known to extend build time even with
#      the cache; this is unavoidable.
export TFCI=py311,linux_x86,public_cache,disk_cache

    int64_t init_timeout_in_ms, TF_Status* status,
    bool clear_existing_contexts);

// Set server def with retries and timeout. This is helpful for fault-tolerant
// initial connection in high-preemption environments, such as
// ParameterServerStrategy training.
// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeoutAndRetries(

// A handle to a tensor on a device.
//
// Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape,
// type etc. Unlike a TF_Tensor, a TFE_TensorHandle may refer to such tensors
// placed in the memory of different devices or remote address spaces.
typedef struct TFE_TensorHandle TFE_TensorHandle;

TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(const TF_Tensor* t,