message: Move to top of "Breaking Changes"
  If this changes causes issues, it can be tricky to debug, so it should be prominently shown in the "Breaking Changes" section
diff: ## Breaking Changes
  * TF Core:
  + * Certain float32 ops run in lower precsion on Ampere based GPUs, including matmuls and convolutions, due to the use of [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/). Specifically, inputs to such ops are rounded from 23 bits of precision to 10
  + bits of precision. This is unlikely to cause issues in practice for deep learning models. In some cases, TensorFloat-32 is also used for complex64 ops.
  + TensorFloat-32 can be disabled by running `tf.config.experimental.enable_tensor_float_32_execution(False)`.
  * The byte layout for string tensors across the C-API has been updated to match TF Core/C++; i.e., a contiguous array of `tensorflow::tstring`/`TF_TString`s.
  * C-API functions `TF_StringDecode`, `TF_StringEncode`, and `TF_StringEncodedSize` are no longer relevant and have been removed; see `core/platform/ctstring.h` for string access/modification in C.
  * `tensorflow.python`, `tensorflow.core` and `tensorflow.compiler` modules are now hidden. These modules are not part of TensorFlow public API.
  * `tf.raw_ops.Max` and `tf.raw_ops.Min` no longer accept inputs of type `tf.complex64` or `tf.complex128`, because the behavior of these ops is not well defined for complex types.
  - * Certain float32 ops run in lower precsion on Ampere based GPUs, including matmuls and convolutions, due to the use of [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/). Specifically, inputs to such ops are rounded from 23 bits of precision to 10
  - bits of precision. This is unlikely to cause issues in practice for deep learning models. In some cases, TensorFloat-32 is also used for complex64 ops.
  - TensorFloat-32 can be disabled by running `config.experimental.enable_tensor_float_32_execution(False)`.
  * XLA:CPU and XLA:GPU devices are no longer registered by default. Use `TF_XLA_FLAGS=--tf_xla_enable_xla_devices` if you really need them, but this flag will eventually be removed in subsequent releases.
  * `tf.keras`:
  