[TF-TRT] Support conversion of FakeQuantWithMinMaxVars in explicit Q/DQ mode
@meena-at-work can you give it a look ? > @meena-at-work can you give it a look ?

I'm just going to add some notes to make everything more clear, then will mark the PR as ready for review in a few minutes, thanks! @meena-at-work @DEKHTIARJonathan All ready now, please take a look when you can, thanks! Hi @meena-at-work / @DEKHTIARJonathan Can you please review this PR ? Thank you! Hi @meena-at-work / @DEKHTIARJonathan Can you please review this PR ? Thank you! Hi @aboubezari, @meena-at-work, @DEKHTIARJonathan I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you!
## Overview
Support the keras quantization API by adding support for the FakeQuantWithMinMaxVars in explicit Q/DQ mode in TF-TRT. This is currently unimplemented in TF-TRT converter code.

## Change summary
### Use explicit precision
**use_explicit_precision** is the indicator if this specific layer is quantized or not. 
With the current implementation, TF-TRT converter will assume that **all** Conv2D layers are quantized in an engine. This is changed to checking if the Conv2D second input is a weight (not explicit) or tensor (explicit). This is more robust since keras training can decide to not quantize some layers.
### Transposing Conv2D input
In the explicit precision mode for Conv2D, the second input (which will be a tensor) is not transposed properly as required by TRT. From my extensive testing, building the cuda engine fails unless we add the transpose **before** adding the `iConvolutionLayer` to the TensorRT network. Will add more notes to make this clear.
### Explicit precision converter for Keras-QAT API
Keras-QAT will use the `FakeQuantWithMinMaxVars` layer, which is not implemented in explicit precision mode. Implemented it using the other explicit converters as an example. Note that we found that `narrow_range` **must** be set to false otherwise conversion fails. See this issue for more details: https://github.com/tensorflow/tensorflow/issues/60168

## Tested
Internally, we were unable to optimize TF-QAT keras models with TensorRT due to the unimplemented issues. With these changes, are able to now convert a  fully quantization-aware trained perception model (1500+ layers) with no performance regressions. 

Please let me know if there are more unittests I can add on the tensorflow side. 