User EncDecRNNTBPEModel with Beam Search
I am using a pre-trained  stt_en_conformer_transducer and it is doing well. However, since my dataset has some special terms I want to use KenLM with Beam Search.

In one tutorial, I saw that we can change the decoding strategy to Beam using: 

```
import copy
decoding_config = copy.deepcopy(asr_model.cfg.decoding) 
# Update the config for the decoding strategy
decoding_config.strategy = "beam"  # Options are `greedy`, `greedy_batch`, `beam`, `tsd` and `alsd`
decoding_config.beam.beam_size = 8  # Increase beam size for better scores, but it will take much longer for transcription !
asr_model.change_decoding_strategy(decoding_config)
asr_model.transcribe(['/home/wesam/wavs/X1.wav'], )

```

It works well, but I did not know how to feed it with my own Language Model? 