QAT training support strided_slice

System information

• TensorFlow version (you are using):2.3.2
• Are you willing to contribute it (Yes/No):

Motivation

What is the use case and how does it broadly benefits users? Prioritization and whether a feature is added is based on how it
helps the community and the feature's maintenance costs.

As examples:

1. Instead of, "Enable the technique for my model," "Enable this technique to work better with standard object detection models, including R-CNN (link) and SSD (link)" is stronger.

2. Instead of, "Try something more customized with the technique," "Implement a variant of the algorithm described in equations X and Y of this paper" is clearer.

Describe the feature

Describe how the feature helps achieve the use case

Describe how existing APIs don't satisfy your use case (optional if obvious)

As examples:

1. You tried using APIs X and Y and were able to do Z. However, that was not sufficient because of ...

2. You achieved your use case with the code snippet W. However, this was more difficult than it should be because of ... (e.g. ran into issue X or had
   to do Y).

The issue names no files, tests, or entry points. First clarify the intended QAT behavior for the TensorFlow strided_slice operation and identify the existing QAT operator-support path; done should include an agreed implementation scope and tests demonstrating strided_slice training support.