internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any
authorization protocol and sends messages unencrypted. It accepts connections
from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access

    cat $BATS_TEST_TMPDIR/actual_missing_licenses
  fi

  # Fail if either of the two "extras" or "missing" lists are present. If so,
  # then the user will see the above error messages.
  [[ ! -s $BATS_TEST_TMPDIR/actual_extra_licenses ]] && [[ ! -s $BATS_TEST_TMPDIR/actual_missing_licenses ]]
}

@test "Pip package generated license includes all dependencies' licenses" {
  do_external_licenses_check \

    cat $BATS_TEST_TMPDIR/actual_missing_licenses
  fi

  # Fail if either of the two "extras" or "missing" lists are present. If so,
  # then the user will see the above error messages.
  [[ ! -s $BATS_TEST_TMPDIR/actual_extra_licenses ]] && [[ ! -s $BATS_TEST_TMPDIR/actual_missing_licenses ]]
}

@test "Pip package generated license includes all dependencies' licenses" {
  do_external_licenses_check \

    enabled by calling `tf.debugging.is_traceback_filtering_enabled()`.

    Note that this feature is only available with Python 3.7 or higher.

    *   Improve the informativeness of error messages raised by Keras
        `Layer.__call__()`, by adding the full list of argument values passed to
        the layer in every exception.

*   Introduce the `tf.compat.v1.keras.utils.track_tf1_style_variables`

contribution and copyright details. If a contributor wants to further mark
their specific copyright on a particular contribution, they should indicate
their copyright solely in the commit message of the change when it is
committed.

LICENSE

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems

# proto-file: devtools/build_cleaner/proto/actions.proto
# proto-message: ActionSpecs

# Python rules should not have more than one source file.
action_spec {
  action: CHECK_FILE_COUNT
  file_count_params {
    rule_selector {
      rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$"
      generator_function_regex: "^(?!boq_header)$"
    }
    max_source_count: 1
  }