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Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.3 to 2.6.4.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.5.3...v2.6.4

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

* Improve applicable batched gemms for bert

Remove std references in runtime compilation since these are not available when using hiprtc and the headers may not be available on the system

* Fuse gemm add with pointwise fusions

As described in #1196, the ONNX mean parser does not work correctly for integral types. This update fixes the issue by handling integral types separately, where summation is performed before division. Additional test cases have also been added for handling integral types.

For clarity on kernel names found when profiling. The new names are set to the order of the ops being compiled. For example: add + relu = add_relu_kernel.

Updated variable names according to #1193

Our documentation indicates a user with sudo can run the install_prereqs.sh file. Turns out that the file is not complete enough to run on Ubuntu 18.04/20.04 independently. I updated the file to resolve the failures.

resolves #1191

Fuse layernorm and added triadd_layernorm fusion.  This is a prep performance booster

ONNX Models changed from master to main. Changing path reflect the proper location

Reverts the dyn_data struct change
Should get around the ambiguous braced initialization list error

Expose add_literal method in C/C++ api

Improves performance for add_gelu.  In bert it is 4x faster and for mul_add it is 50% faster than what we current have.