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

dill==0.3.7 \
    --hash=sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e \
    --hash=sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03
    # via -r requirements.in
dm-tree==0.1.8 \
    --hash=sha256:054b461f8176f4bce7a21f7b1870f873a1ced3bdbe1282c816c550bb43c71fa6 \
    --hash=sha256:0d3172394079a86c3a759179c65f64c48d1a42b89495fcf38976d11cc3bb952c \

    testsuites.attrib = suite_attrib
    testsuite.attrib = suite_attrib
    indent_xml(testsuites)

  tree = ElemTree.ElementTree(testsuites)
  file_path = os.path.join(output_path)
  with open(file_path, 'wb') as f:
    f.write(b'<?xml version="1.0"?>\n')
    tree.write(f)
    if verbose:
      print(f'\nWrote XML with Bazel invocation results to {file_path}')

    validations:
      required: true

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TF_CAPI_EXPORT extern void TF_DeleteDir(const char* dirname, TF_Status* status);

// Deletes the specified directory and all subdirectories and files underneath
// it. This is accomplished by traversing the directory tree rooted at dirname
// and deleting entries as they are encountered.
//
// If dirname itself is not readable or does not exist, *undeleted_dir_count is
// set to 1, *undeleted_file_count is set to 0 and an appropriate status (e.g.

## Resources

*   [TensorFlow.org](https://www.tensorflow.org)
*   [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
*   [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
*   [TensorFlow Examples](https://github.com/tensorflow/examples)
*   [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)
*   [TensorFlow Blog](https://blog.tensorflow.org)

dill==0.3.7 \
    --hash=sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e \
    --hash=sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03
    # via -r requirements.in
dm-tree==0.1.8 \
    --hash=sha256:054b461f8176f4bce7a21f7b1870f873a1ced3bdbe1282c816c550bb43c71fa6 \
    --hash=sha256:0d3172394079a86c3a759179c65f64c48d1a42b89495fcf38976d11cc3bb952c \

dill==0.3.7 \
    --hash=sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e \
    --hash=sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03
    # via -r requirements.in
dm-tree==0.1.8 \
    --hash=sha256:054b461f8176f4bce7a21f7b1870f873a1ced3bdbe1282c816c550bb43c71fa6 \
    --hash=sha256:0d3172394079a86c3a759179c65f64c48d1a42b89495fcf38976d11cc3bb952c \

build/installation issues on GitHub.

-   type: markdown
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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
[requirements_updater](https://github.com/tensorflow/tensorflow/tree/master/ci/official/requirements_updater)
tool using the path to this wheel file.

### Hermetic Python

For details about hermetic Python and setting its toolchain version, see