How to use the medcat.utils.data_utils.encode_category_values function in medcat

# Prepare the data
        data = prepare_from_json(data, self.cntx_left, self.cntx_right, self.tokenizer, lowercase=lowercase, tui_filter=tui_filter)

        if category_name is not None:
            self.category_name = category_name

        # Check is the name there
        if self.category_name not in data:
            raise Exception("The category name does not exist in this json file.")

        data = data[self.category_name]

        if not fine_tune:
            # Encode the category values
            data, self.category_values = encode_category_values(data)
            self.i_category_values = {v: k for k, v in self.category_values.items()}
        else:
            # We already have everything, just get the data
            data, _ = encode_category_values(data, vals=self.category_values)

        # Convert data tkns to ids
        data = tkns_to_ids(data, self.tokenizer)

        if not fine_tune:
            if model_name == 'lstm':
                from medcat.utils.models import LSTM
                nclasses = len(self.category_values)
                bid = model_config.get("bid", True)
                num_layers = model_config.get("num_layers", 2)
                input_size = model_config.get("input_size", 300)
                hidden_size = model_config.get("hidden_size", 300)

if category_name is not None:
            self.category_name = category_name

        # Check is the name there
        if self.category_name not in data:
            raise Exception("The category name does not exist in this json file.")

        data = data[self.category_name]

        if not fine_tune:
            # Encode the category values
            data, self.category_values = encode_category_values(data)
            self.i_category_values = {v: k for k, v in self.category_values.items()}
        else:
            # We already have everything, just get the data
            data, _ = encode_category_values(data, vals=self.category_values)

        # Convert data tkns to ids
        data = tkns_to_ids(data, self.tokenizer)

        if not fine_tune:
            if model_name == 'lstm':
                from medcat.utils.models import LSTM
                nclasses = len(self.category_values)
                bid = model_config.get("bid", True)
                num_layers = model_config.get("num_layers", 2)
                input_size = model_config.get("input_size", 300)
                hidden_size = model_config.get("hidden_size", 300)
                dropout = model_config.get("dropout", 0.5)

                self.model = LSTM(self.embeddings, self.pad_id, nclasses=nclasses, bid=bid, num_layers=num_layers,
                             input_size=input_size, hidden_size=hidden_size, dropout=dropout)