V15.B16, V1.B16 AESMC V1.B16, V1.B16 AESE V16.B16, V0.B16 AESMC V0.B16, V0.B16 AESE V16.B16, V1.B16 AESMC V1.B16, V1.B16 AESE V17.B16, V0.B16 AESMC V0.B16, V0.B16 AESE V17.B16, V1.B16 AESMC V1.B16, V1.B16 AESE V18.B16, V0.B16 AESMC V0.B16, V0.B16 AESE V18.B16, V1.B16 AESMC V1.B16, V1.B16 AESE V19.B16, V0.B16 AESMC V0.B16, V0.B16 AESE V19.B16, V1.B16 AESMC V1.B16, V1.B16 AESE V20.B16, V0.B16 AESMC V0.B16, V0.B16 AESE V20.B16, V1.B16 AESMC V1.B16, V1.B16 AESE V21.B16, V0.B16 AESE V21.B16, V1.B16 VEOR V0.B16,...

  - kubelet v1.22.0 - v1.22.1
  - kubelet v1.21.0 - v1.21.4
  - kubelet v1.20.0 - v1.20.10
  - kubelet <= v1.19.14

**Fixed Versions**:
  - kubelet v1.22.2
  - kubelet v1.21.5
  - kubelet v1.20.11
  - kubelet v1.19.15

This vulnerability was reported by Fabricio Voznika and Mark Wolters of Google.

    *   Add NUMA-aware MapAndBatch dataset.
    *   Deprecate `tf.data.Dataset.make_one_shot_iterator()` in V1, removed it
        from V2, and added tf.compat.v1.data.make_one_shot_iterator()`.
    *   Deprecate `tf.data.Dataset.make_initializable_iterator()` in V1, removed
        it from V2, and added `tf.compat.v1.data.make_initializable_iterator()`.
    *   Enable nested dataset support in core `tf.data` transformations.

### Pydantic v1

**This version of FastAPI still supports Pydantic v1**. And although Pydantic v1 will be deprecated at some point, it will still be supported for a while.

This means that you can install the new Pydantic v2, and if something fails, you can install Pydantic v1 while you fix any problems you might have, but having the latest FastAPI.