Thanks for the help. I got it to work.
def load_pipeline(MODEL_NAME: str) -> object:

    """
    Load a saved PyTorch model
    :param MODEL_NAME:  Name of the model to parse
    :return:
    """

    model = LSTM.AudioLSTM(
        num_layer=config.Model.NUM_LAYERS,
        input_size=config.Model.INPUT_SIZE,
        hidden_size=config.Model.HIDDEN_DIM,
        output_size=config.Model.OUTPUT_SIZE,
        dropout=config.Model.DROPOUT,
    )
    model.eval()
    file_path = os.path.join(config.TRAINED_MODEL_DIR, MODEL_NAME + _version + ".pt")
    return model, file_path

model, path  = load_pipeline(NAME)

model.load_state_dict(torch.load(path))
<All keys matched successfully>
model.eval()
AudioLSTM(
  (RNN_TYPE): LSTM(512, 1, num_layers=3, batch_first=True, dropout=0.3, bidirectional=True)
  (dropout): Dropout(p=0.3, inplace=False)
  (fc): Linear(in_features=1, out_features=2, bias=True)
  (out): Sigmoid()
)