Fine-tune stt_en_conformer_ctc_large
**Describe the bug**

I am trying to fine-tune **stt_en_conformer_ctc_large** model using the following:

`asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_large")`

Then I set the training params:

```
# not working: 
from omegaconf import OmegaConf, open_dict
    
params = asr_model.cfg 
new_opt = copy.deepcopy(params.optim)
new_opt.lr = 0.0001

params['train_ds']['manifest_filepath'] = train_manifest_path
params['train_ds']['batch_size'] = 8


params['validation_ds']['manifest_filepath'] = dev_manifest_path
params['validation_ds']['batch_size'] = 8

# Use the smaller learning rate we set before
asr_model.setup_optimization(optim_config=new_opt)

# Point to the new training data for fine-tuning
asr_model.setup_training_data(train_data_config=params['train_ds'])


# Point to the new validation data for fine-tuning
asr_model.setup_validation_data(val_data_config=params['validation_ds'])
```

Received error:

```
~/NeMo/nemo/collections/asr/models/ctc_models.py in setup_training_data(self, train_data_config)
    423             # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
    424             # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
--> 425             if isinstance(self._trainer.limit_train_batches, float):
    426                 self._trainer.limit_train_batches = int(
    427                     self._trainer.limit_train_batches

AttributeError: 'NoneType' object has no attribute 'limit_train_batches'

```

However, it **works** when I load the config from the config_bpe.yaml:
```
from omegaconf import OmegaConf, open_dict

# ! wget -P configs/ https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/asr/conf/citrinet/config_bpe.yaml --no-check-certificate
    
params = OmegaConf.load('configs/config_bpe.yaml')
    
new_opt = copy.deepcopy(params.model.optim)
new_opt.lr = 0.0001

params['model']['train_ds']['manifest_filepath'] = train_manifest_path
params['model']['train_ds']['batch_size'] = 8


params['model']['validation_ds']['manifest_filepath'] = dev_manifest_path
params['model']['validation_ds']['batch_size'] = 8

# Use the smaller learning rate we set before
asr_model.setup_optimization(optim_config=new_opt)

# Point to the new validation data for fine-tuning
asr_model.setup_validation_data(val_data_config=params['model']['validation_ds'])


# Point to the data we'll use for fine-tuning as the training set
asr_model.setup_training_data(train_data_config=params['model']['train_ds'])

```

What is the correct procedure?