Quantization for CitriNet-1024-Gamma-0.25 
**Describe the bug**
I was trying to convert the CitriNet-1024-Gamma-0.25 to TensorRT Quantized Model, while following the example workflow given under the quantization section but facing the following issue

**Steps/Code to reproduce bug**

root@nvidia-DGX-Station:/workspace/nemo/NeMo/examples/asr/quantization# python3 speech_to_text_calibrate.py --asr_model=citrinet-1024-gamma-0.25.nemo --dataset=test_hindi_navana.json 
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
###          (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
###          (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################

[NeMo W 2021-09-16 14:04:05 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torchaudio-0.7.0a0+42d447d-py3.8-linux-x86_64.egg/torchaudio/backend/utils.py:53: UserWarning: "sox" backend is being deprecated. The default backend will be changed to "sox_io" backend in 0.8.0 and "sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.
      warnings.warn(
    
[NeMo W 2021-09-16 14:04:06 experimental:27] Module <class 'nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2021-09-16 14:04:07 speech_to_text_calibrate:80] Using local ASR model from citrinet-1024-gamma-0.25.nemo
[NeMo I 2021-09-16 14:04:19 mixins:147] Tokenizer SentencePieceTokenizer initialized with 72 tokens
[NeMo W 2021-09-16 14:04:19 modelPT:138] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
    Train config : 
    manifest_filepath: ./manifest/hindi/train_hindi_data.json
    sample_rate: 16000
    batch_size: 16
    trim_silence: true
    max_duration: 20.0
    shuffle: true
    is_tarred: false
    tarred_audio_filepaths: null
    use_start_end_token: true
    num_workers: 8
    pin_memory: true
    
[NeMo W 2021-09-16 14:04:19 modelPT:145] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). 
    Validation config : 
    manifest_filepath: ./manifest/hindi/test_hindi_data.json
    sample_rate: 16000
    batch_size: 8
    shuffle: false
    use_start_end_token: true
    num_workers: 8
    pin_memory: true
    trim_silence: true
    
[NeMo W 2021-09-16 14:04:19 modelPT:151] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
    Test config : 
    manifest_filepath: ./manifest/hindi/test_hindi_data.json
    sample_rate: 16000
    batch_size: 8
    shuffle: false
    use_start_end_token: true
    num_workers: 8
    pin_memory: true
    trim_silence: true
    
[NeMo I 2021-09-16 14:04:19 features:252] PADDING: 16
[NeMo I 2021-09-16 14:04:19 features:269] STFT using torch
I0916 14:04:20.338965 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.339134 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.339357 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.339587 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.341764 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.341858 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.342052 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.342261 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.343992 140410652182336 _utils.py:126] Input is fake quantized to 8 bits in QuantAdaptiveAvgPool1d with axis None!
I0916 14:04:20.344176 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
[NeMo W 2021-09-16 14:04:20 modelPT:138] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
    Train config : 
    manifest_filepath: ./manifest/hindi/train_hindi_data.json
    sample_rate: 16000
    batch_size: 16
    trim_silence: true
    max_duration: 20.0
    shuffle: true
    is_tarred: false
    tarred_audio_filepaths: null
    use_start_end_token: true
    num_workers: 8
    pin_memory: true
    
[NeMo W 2021-09-16 14:04:20 modelPT:145] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). 
    Validation config : 
    manifest_filepath: ./manifest/hindi/test_hindi_data.json
    sample_rate: 16000
    batch_size: 8
    shuffle: false
    use_start_end_token: true
    num_workers: 8
    pin_memory: true
    trim_silence: true
    
[NeMo W 2021-09-16 14:04:20 modelPT:151] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
    Test config : 
    manifest_filepath: ./manifest/hindi/test_hindi_data.json
    sample_rate: 16000
    batch_size: 8
    shuffle: false
    use_start_end_token: true
    num_workers: 8
    pin_memory: true
    trim_silence: true
    
[NeMo I 2021-09-16 14:04:20 features:252] PADDING: 16
[NeMo I 2021-09-16 14:04:20 features:269] STFT using torch
I0916 14:04:20.691268 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.691397 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.691546 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.691687 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.693689 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.693774 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.693904 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.694039 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.695321 140410652182336 _utils.py:126] Input is fake quantized to 8 bits in QuantAdaptiveAvgPool1d with axis None!
I0916 14:04:20.695476 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
    return target(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/conv_asr.py", line 166, in __init__
    JasperBlock(
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 658, in __init__
    SqueezeExcite(
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 342, in __init__
    self.change_context_window(context_window=context_window)
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 411, in change_context_window
    if not isinstance(self.pool, quant_nn.QuantAdaptiveAvgPool1d(1)):
TypeError: isinstance() arg 2 must be a type or tuple of types

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "speech_to_text_calibrate.py", line 160, in <module>
    main()  # noqa pylint: disable=no-value-for-parameter
  File "speech_to_text_calibrate.py", line 84, in main
    asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model, override_config_path=asr_model_cfg)
  File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 481, in restore_from
    return cls._default_restore_from(restore_path, override_config_path, map_location, strict, return_config)
  File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 434, in _default_restore_from
    instance = cls.from_config_dict(config=conf)
  File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 472, in from_config_dict
    instance = cls(cfg=config)
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 155, in __init__
    self.encoder = EncDecCTCModel.from_config_dict(self._cfg.encoder)
  File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 437, in from_config_dict
    instance = hydra.utils.instantiate(config=config)
  File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 180, in instantiate
    return instantiate_node(config, *args, recursive=_recursive_, convert=_convert_)
  File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 249, in instantiate_node
    return _call_target(target, *args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 64, in _call_target
    raise type(e)(
  File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
    return target(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/conv_asr.py", line 166, in __init__
    JasperBlock(
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 658, in __init__
    SqueezeExcite(
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 342, in __init__
    self.change_context_window(context_window=context_window)
  File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 411, in change_context_window
    if not isinstance(self.pool, quant_nn.QuantAdaptiveAvgPool1d(1)):
TypeError: Error instantiating 'nemo.collections.asr.modules.conv_asr.ConvASREncoder' : isinstance() arg 2 must be a type or tuple of types

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
NGC: nvcr.io/nvidia/nemo:1.2.0 

**Additional context**

Add any other context about the problem here.
Example: V100
