def METHOD_NAME(self):
    asr_model = ASRModel.from_pretrained("stt_en_conformer_ctc_small")
    asr_model = asr_model.eval()
    len = 16000 * 60 * 30  # 30 minutes, OOM without local attention
    input_signal_long = torch.randn(size=(1, len), device=asr_model.device)
    length_long = torch.tensor([len], device=asr_model.device)
    # switch to local attn
    asr_model.change_attention_model(self_attention_model="rel_pos_local_attn", att_context_size=(64, 64))
    with torch.no_grad():
        asr_model.forward(input_signal=input_signal_long, input_signal_length=length_long)
    # switch context size only (keep local)
    asr_model.change_attention_model(att_context_size=(192, 192))
    with torch.no_grad():
        asr_model.forward(input_signal=input_signal_long, input_signal_length=length_long)