Duplex Model for Inverse Text Norm (ITN) does not work
I follow the tutorial of inference the pretrained checkpoint as in [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/neural_text_normalization_t5) and [here](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/text_normalization/nn_text_normalization.html).

The TN option works just fine but when I use the mode of `ITN`, the model still output the Text Normalization instead.

The command I used:
```
cd NeMo/examples/nlp/duplex_text_normalization;

# run inference in interactive mode using pretrained tagger and decoder models
python duplex_text_normalization_infer.py \
    tagger_pretrained_model=neural_text_normalization_t5 \
    decoder_pretrained_model=neural_text_normalization_t5 \
    inference.from_file=False \
    lang=en \
    mode=itn
```

The output:
```
Entering interactive mode.
Type "STOP" to exit.
Input a test input:$123
Skipping post-processing of one hundred twenty three dollars for '$'
Prediction (ITN): one hundred twenty three dollars
Type "STOP" to exit.
Input a test input:one hundred twenty three
Prediction (ITN): one hundred twenty three
Type "STOP" to exit.
Input a test input:one two three
Prediction (ITN): one two three
Type "STOP" to exit.
Input a test input:one hundred twenty three dollars
Prediction (ITN): one hundred twenty three dollars
```

As you can see, the model still returns TN result.