`SavedModel`) and executes them in parallel on available executors. Running
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

`reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`
becomes `axis` * `tf.reverse_sequence`: `batch_dim` becomes `batch_axis`,
`seq_dim` becomes `seq_axis` * `tf.sparse_concat`: `concat_dim` becomes `axis` *
`tf.sparse_reduce_sum`: `reduction_axes` becomes `axis` *
`tf.sparse_reduce_sum_sparse`: `reduction_axes` becomes `axis` *

    given-names: Martin
  - family-names: Yu
    given-names: Yuan
  - family-names: Zheng
    given-names: Xiaoqiang
identifiers:
  - type: doi
    value: 10.5281/zenodo.4724125
    description: The concept DOI for the collection containing all versions of the Citation File Format.
date-released: "2015-11-09"
license: "Apache-2.0"
doi: 10.5281/zenodo.4724125