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ci/official/wheel_test/README.md

5. Moves the updated `requirements_lock_<python_version>.txt` file
to the `../wheel_test/` directory.


### How it Works in the Presubmit Job

`_requirements_lock` files will be generated by the presubmit job. A detailed
description will be provided once it's integrated into presubmit.

### test_import_api_packages

This Python test verifies whether the API v2 packages can be imported from the

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github.com/tensorflow/tensorflow

ci/official/utilities/cleanup_summary.sh

set -euxo pipefail

function resultstore_extract_fallback {
  # In case the main script fails somehow.
  cat <<EOF
IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you
can view more detailed results that are probably easier to read than this log.
Try the links below:
EOF
  # Find any "Streaming build results to" line, then print the last word in it,
  # and don't print duplicates

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github.com/tensorflow/tensorflow

ci/official/requirements_updater/README.md

Requirements updater is a standalone tool, intended to simplify process of
updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed
requirements file for each version, with hashes specified for each
dependency required, as well as their sub-dependencies.

### How to update/add requirements

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github.com/tensorflow/tensorflow

SECURITY.md

happening (e.g., rate limits, ACLs, monitors to restart broken servers).

### Model sharing

If the multitenant design allows sharing models, make sure that tenants and
users are aware of the security risks detailed here and that they are going to
be practically running code provided by other users. Currently there are no good
ways to detect malicious models/graphs/checkpoints, so the recommended way to

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github.com/tensorflow/tensorflow

ci/official/utilities/generate_index_html.sh

# Usage: generate_index_html.sh /path/to/output/index.html

cat > "$1" <<EOF
<html>
<head>
<title>$(basename "$KOKORO_JOB_NAME")</title>
</head>
<body>
<h1>TensorFlow Job Logs and Links</h1>
<h2>Job Details</h2>
<ul>
<li>Job name: $KOKORO_JOB_NAME</li>
<li>Job pool: $KOKORO_JOB_POOL</li>
<li>Job ID: $KOKORO_BUILD_ID</li>
<li>Current HEAD Piper Changelist, if any: cl/${KOKORO_PIPER_CHANGELIST:-not available}</li>

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github.com/tensorflow/tensorflow

ci/official/utilities/extract_resultstore_links.py

                       'matching the invocation, inside the log. '
                       'Please look for it in the build log.\n\n')
      failure_msg += (
          f'See the ResultStore link for a detailed view of failed targets:\n'
          f'{url}\n\n')
      failure_msg += (
          f'Here\'s a fragment of the log containing the failure:\n\n'
          f'[ ... TRUNCATED ... ]\n\n'

Python

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github.com/tensorflow/tensorflow

tensorflow/c/experimental/filesystem/filesystem_interface.h

  ///   * `statuses` is either null or an array of `num_files` non-null elements
  ///     of type `TF_Status*`.
  ///
  /// If `statuses` is not null, plugins must fill each element with detailed
  /// status for each file, as if calling `path_exists` on each one. Core
  /// TensorFlow initializes the `statuses` array and plugins must use
  /// `TF_SetStatus` to set each element instead of directly assigning.
  ///

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github.com/tensorflow/tensorflow

tensorflow/c/eager/tape.h

      gtl::ArraySlice<Gradient*> output_gradients, absl::Span<Gradient*> result,
      bool build_default_zeros_grads = true);

  // Whether the tape is persistent. See ctor for detailed description.
  bool IsPersistent() const { return persistent_; }

 private:
  TensorTape tensor_tape_;
  OpTape<BackwardFunction, TapeTensor> op_tape_;
  int64_t next_op_id_{0};

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github.com/tensorflow/tensorflow

RELEASE.md

    which is a NumPy-compatible API for writing TF programs. See the
    [detailed guide](https://www.tensorflow.org/guide/tf_numpy) to learn more.
    Additional details below.

*   Adds Support for
    [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/)
    on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for

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github.com/tensorflow/tensorflow

.github/workflows/scorecards-analysis.yml

        with:
          results_file: results.sarif
          results_format: sarif
          # Publish the results to enable scorecard badges. For more details, see
          # https://github.com/ossf/scorecard-action#publishing-results.
          # For private repositories, `publish_results` will automatically be set to `false`,
          # regardless of the value entered here.

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