def METHOD_NAME(self, args):
    """`initialize` is called only once when the model is being loaded.
    Implementing `initialize` function is optional. This function allows
    the model to initialize any state associated with this model.
    Parameters
    ----------
    args : dict
      Both keys and values are strings. The dictionary keys and values are:
      * model_config: A JSON string containing the model configuration
      * model_instance_kind: A string containing model instance kind
      * model_instance_device_id: A string containing model instance device ID
      * model_repository: Model repository path
      * model_version: Model version
      * model_name: Model name
    """
    self.model_config = model_config = json.loads(args['model_config'])
    self.max_batch_size = max(model_config["max_batch_size"], 1)
    if "GPU" in model_config["instance_group"][0]["kind"]:
        self.device = "cuda"
    else:
        self.device = "cpu"
    # Get OUTPUT0 configuration
    output0_config = pb_utils.get_output_config_by_name(
        model_config, "speech")
    # Convert Triton types to numpy types
    output0_dtype = pb_utils.triton_string_to_numpy(
        output0_config['data_type'])
    if output0_dtype == np.float32:
        self.output0_dtype = torch.float32
    else:
        self.output0_dtype = torch.float16
    # Get OUTPUT1 configuration
    output1_config = pb_utils.get_output_config_by_name(
        model_config, "speech_lengths")
    # Convert Triton types to numpy types
    self.output1_dtype = pb_utils.triton_string_to_numpy(
        output1_config['data_type'])
    params = self.model_config['parameters']
    opts = kaldifeat.FbankOptions()
    opts.frame_opts.dither = 0
    for li in params.items():
        key, value = li
        value = value["string_value"]
        if key == "num_mel_bins":
            opts.mel_opts.num_bins = int(value)
        elif key == "frame_shift_in_ms":
            opts.frame_opts.frame_shift_ms = float(value)
        elif key == "frame_length_in_ms":
            opts.frame_opts.frame_length_ms = float(value)
        elif key == "sample_rate":
            opts.frame_opts.samp_freq = int(value)
    opts.device = torch.device(self.device)
    self.opts = opts
    self.feature_extractor = Fbank(self.opts)
    self.feature_size = opts.mel_opts.num_bins