How to do quantization on the ASR model
**Describe the bug**

I am trying to do Post training static quantization on ASR model, following example here: (https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#post-training-static-quantization) but keeps on getting following error. 
Two questions: 
1) Is there a way to bypass this error and get this working?
2) How to know what is the input shape for "stt_en_conformer_ctc_xlarge" model? Is there any API through which I know the shape?


```
File "asr_ctc.py", line 20, in <module>
    print(model.transcribe([audio_filepath], batch_size=1)[0])
 ---
 ----
 ---
 NotImplementedError: Could not run 'quantized::conv2d.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::conv2d.new' is only available for these backends: [Negative, UNKNOWN_TENSOR_TYPE_ID, QuantizedXPU, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, SparseCPU, SparseCUDA, SparseHIP, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, SparseVE, UNKNOWN_TENSOR_TYPE_ID, NestedTensorCUDA, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID].
```

**Steps/Code to reproduce bug**
```
# Streaming with CTC based models
import torch 
import nemo.collections.asr as nemo_asr

device = 'cpu'
model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("stt_en_conformer_ctc_xlarge", map_location=device)
model = model.to(device)
model.eval()
audio_filepath = './datasets/mini-dev-clean/LibriSpeech/dev-clean-2-processed/2428-83699-0042.wav'
model.qconfig = torch.quantization.get_default_qconfig()
torch.quantization.prepare(model, inplace=True)
torch.quantization.convert(model, inplace=True)
print("The transcribed speech is: ")
print(model.transcribe([audio_filepath], batch_size=1)[0])

```

**Expected behavior**

Expected transcribed output 

**Environment overview (please complete the following information)**

 - Environment location: [Bare-metal]
 - Method of NeMo install: [pip install Nemo].

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 22.04
- PyTorch = 1.12.1
- Python version = 3.8

**Additional context**

Add any other context about the problem here.
Example: CPU model
