## 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

# limitations under the License.
# ==============================================================================
# Sourcing this enables Bazel remote cache (read and write)
# Note that "_push" cache configs write to GCS buckets and require
# authentication. If you are not a Googler, source "public_cache" to enable the
# public read-only cache.
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then

   `release_updater.sh`

5) Run the `updater.sh` shell script. \
   If the base requirements file hasn't yet been updated to account for the new
   Python version, which will require different versions for at least some
   dependencies, it will need to be updated now, for the script to run
   successfully.

6) A new `requirements_lock_3_11.txt` file should appear under the root of the

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
recommendation is not to serve models using Eager mode since it also has
performance limitations.

*   Include unit tests when you contribute new features, as they help to a)
    prove that your code works correctly, and b) guard against future breaking
    changes to lower the maintenance cost.
*   Bug fixes also generally require unit tests, because the presence of bugs
    usually indicates insufficient test coverage.
*   Keep API compatibility in mind when you change code in core TensorFlow,
    e.g., code in

        "//tensorflow/c:tf_file_statistics",
        "//tensorflow/c:tf_status",
        "//tensorflow/c/experimental/filesystem:filesystem_interface",
    ],
)

# Since building pip package and API tests require a filesystem, we provide a
# static registration target that they should link against.
cc_library(
    name = "posix_filesystem_static",
    srcs = ["posix_filesystem_static.cc"],
    visibility = ["//visibility:public"],

    installed from options: - source - binary validations: required: true

-   type: input id: tfversion attributes: label: Tensorflow Version description:
    placeholder: ex,. tf 2.8 validations: required: true

-   type: dropdown id: Code attributes: label: Custom Code description:
    options: - "Yes" - "No" validations: required: true

-   type: input id: OS attributes: label: OS Platform and Distribution

# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.