"use_cer" in ASR training with pytorch lighnting.
The CER value during the validation of the training process does not align with the inference process. And during the inference process, the execution is like the following part.
`wer_value = word_error_rate(hypotheses=hypotheses, references=references, use_cer=args.use_cer)`.
Meanwhile, the training codes are like:

```
@hydra_runner(config_path="conf_decay", config_name="config")
def main(cfg):
    profiler = PyTorchProfiler(emit_nvtx=True)
    trainer = pl.Trainer(**cfg.trainer,profiler=profiler)
    exp_manager(trainer, cfg.get("exp_manager", None))
    asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
    pretrain_model = cfg.get("pretrain_model", None)
    if pretrain_model:
        if pretrain_model.endswith(".nemo"):
            asr_model = EncDecCTCModel.restore_from(pretrain_model)
        elif pretrain_model.endswith(".ckpt"):
            asr_model = EncDecCTCModel.load_from_checkpoint(pretrain_model)
        asr_model.setup_optimization(optim_config=cfg.model.optim)
        asr_model.setup_training_data(train_data_config=cfg.model.train_ds)
        asr_model.setup_validation_data(val_data_config=cfg.model.validation_ds)
    trainer.fit(asr_model)
    nvtx.range_pop()
    save_model = cfg.get("save_model", "quartznet.nemo")
    asr_model.save_to(save_model)
```

And the script for the execution like the following:
`python /ws/nemo/speech_to_text.py \
...
+use_cer=True
...`
The usage of parameter 'use_cer' is right? I didn't find any deep explanation for this parameter in Nemo reference documents.  

