TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

Since any tenants or users providing models, graphs or checkpoints can execute
code in context of the TensorFlow service, it is important to design isolation

            "-Wl,-unexported_symbols_list,$(location //tensorflow:tf_private_symbols.lds)",
        ],
        "//tensorflow:windows": [],
        "//tensorflow:freebsd": [
            "-Wl,--version-script,$(location //tensorflow:tf_framework_version_script.lds)",
            "-lexecinfo",
        ],
        "//conditions:default": [
            "-Wl,--version-script,$(location //tensorflow:tf_framework_version_script.lds)",

        previous gradient implementation.
    *   Added a new session config setting `internal_fragmentation_fraction`,
        which controls when the BFC Allocator needs to split an oversized chunk
        to satisfy an allocation request.
    *   Added `tf.get_current_name_scope()` which returns the current full name
        scope string that will be prepended to op names.
*   `tf.data`: