Use model-cast-to-bfloat16 rather than AMP-to-bfloat16 for inference.
FYI, the PR description has been updated to describe the benefit from using this mode of execution instead of AMP This is ready for another round of review. Unfortunately the black + isort change made a lot of noise. But basically I removed a lot of cruft. This PR basically does only three things now:

1. Modify transcribe_speech.py to run using casting to float16/bfloat16 instead of AMP
1. Modify transcribe_speech.py to output RTFx scores, using the calculate_rtfx=True config. It is set to false by default (but I wouldn't be opposed to setting it to True by default later for the sake of educating users about their throughput results).
1. Make small changes to the positional encoding buffer and a call to masked_fill() to make sure that data is not accidentally casted up to float32. @nithinraok @pzelasko @titu1994 I think this change is good to go at this point. Looks good to me!
# What does this PR do ?

I demonstrate, using transcribe_speech.py, that simply casting the entire model to bfloat16 gives about 15% higher performance than using automatic mixed precision. The reasons why are discussed in #9086

There are a few small modifications to our conformer encoder and multi head attention implementations to make this work. Basically, torch.float32 was unintentionally used at a few points.

I also disable updates to the batch norm statistics in this PR during inference. See the relevant comment in module.py.

Note that casting the preprocessor's input waveform to bfloat16 causes a serious accuracy degredation. I provide a warning about this. It is better to just do the preprocessor in float32, and then cast its output to bfloat16 (which is what I do).

I have verified that this works with Parakeet CTC 1.1B and Parakeet RNN-T 1.1B. I will upload a table showing WER and RTFx throughput when running transcribe_speech.py, using the new casting method and the old AMP method. RTFx improves by about 15%, and WER stays about the same.

This image demonstrates (1) the increase in RTFx across a range of datasets and (2) the fact that WER does not degrade.

<img width="1130" alt="image" src="https://github.com/NVIDIA/NeMo/assets/4767568/aa5b13e7-d496-4f78-a488-a14cd33168bd">


**Collection**: ASR

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* See transcribe_speech.py. Note that several parts of the code use AMP rather than simply casting the model.

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# Additional Information
* Related to #9086
