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  1. ci/official/bisect.sh

    #   export TF_BISECT_GOOD=a_good_commit_sha
    #   export TF_BISECT_BAD=a_failing_commit_sha
    #   export TF_ANY_TARGETS="quoted list of targets, like on the command line"
    #   export TF_ANY_MODE=test
    set -euxo pipefail
    cd "$(dirname "$0")/../../"  # tensorflow/
    export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
    git bisect start "$TF_BISECT_BAD" "$TF_BISECT_GOOD"
    Shell Script
    - Registered: Tue May 07 12:40:20 GMT 2024
    - Last Modified: Thu Feb 01 03:21:19 GMT 2024
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  2. CODE_OF_CONDUCT.md

    Project Steward will determine whether the Code of Conduct was violated, and will issue an appropriate sanction, possibly including a written warning or expulsion from the project, project sponsored spaces, or project forums. We ask that you make a good-faith effort to resolve your conflict via the conflict resolution policy before submitting a report.
    
    Violations of the Code of Conduct can occur in any setting, even those unrelated to the project. We will only consider complaints about conduct...
    Plain Text
    - Registered: Tue May 07 12:40:20 GMT 2024
    - Last Modified: Fri Feb 05 18:43:16 GMT 2021
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  3. .github/bot_config.yml

             * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
             * All you need is a good internet connection and you are all set.
          * Try to build TF from sources by changing CPU optimization flags.
       
       *Please let us know if this helps.*
       
    windows_comment: >
    Others
    - Registered: Tue May 07 12:40:20 GMT 2024
    - Last Modified: Tue Oct 17 11:48:07 GMT 2023
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  4. SECURITY.md

    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
    mitigate the risk in this scenario is to sandbox the model execution.
    
    ### Hardware attacks
    
    Plain Text
    - Registered: Tue May 07 12:40:20 GMT 2024
    - Last Modified: Sun Oct 01 06:06:35 GMT 2023
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  5. .github/workflows/stale-issues.yml

              close-issue-reason: completed
              # List of labels to remove when issues/PRs unstale. 
              labels-to-remove-when-unstale: "stat:contribution welcome,stat:good first issue"
              any-of-labels: "stat:contribution welcome,stat:good first issue"
              stale-issue-message: > 
                This issue is stale because it has been open for 180 days with no activity.
    Others
    - Registered: Tue May 07 12:40:20 GMT 2024
    - Last Modified: Thu Nov 23 20:04:38 GMT 2023
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  6. ci/official/containers/linux_arm64/build.sh

    cd "$(dirname "$0")"
    
    if is_continuous_or_release || [[ -z "$KOKORO_BUILD_ID" ]]; then
      # A continuous job is the only one to publish to latest
      TAG="latest-multi-python"
    else
      # If it is a change, grab a good tag for iterative builds
      if [[ -z "${KOKORO_GITHUB_PULL_REQUEST_NUMBER}" ]]; then
        TAG=$(head -n 1 "$KOKORO_PIPER_DIR/presubmit_request.txt" | cut -d" " -f2)
      else
        TAG="pr-${KOKORO_GITHUB_PULL_REQUEST_NUMBER}"
      fi
    Shell Script
    - Registered: Tue May 07 12:40:20 GMT 2024
    - Last Modified: Fri Nov 03 13:38:49 GMT 2023
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