def METHOD_NAME(self, data):
    """
    The input values for the DLRM model are twofold. There is a dense part and a sparse part.
    The sparse part consists of a list of ids where each entry can consist of zero, one or multiple ids.
    Due to the inconsistency in elements, the sparse part is represented by the KeyJaggedTensor class provided by TorchRec.
    Args:
        data (str): The input data is in the form of a string
    Returns:
        Tuple of:
            (Tensor): Dense features
            (KeyJaggedTensor): Sparse features
    """
    float_features, id_list_features_lengths, id_list_features_values = [], [], []
    for row in data:
        input = row.get("data") or row.get("body")
        if not isinstance(input, dict):
            input = json.loads(input)
        # This is the dense feature part
        assert "float_features" in input
        # The sparse input consists of a length vector and the values.
        # The length vector contains the number of elements which are part fo the same entry in the linear list provided as input.
        assert "id_list_features.lengths" in input
        assert "id_list_features.values" in input
        float_features.append(input["float_features"])
        id_list_features_lengths.extend(input["id_list_features.lengths"])
        id_list_features_values.append(input["id_list_features.values"])
    # Reformat the values input for KeyedJaggedTensor
    id_list_features_values = torch.FloatTensor(id_list_features_values)
    id_list_features_values = torch.transpose(id_list_features_values, 0, 1)
    id_list_features_values = [value for value in id_list_features_values]
    # Dense and Sparse Features for DLRM model
    dense_features = torch.FloatTensor(float_features)
    sparse_features = KeyedJaggedTensor(
        keys=DEFAULT_CAT_NAMES,
        lengths=torch.LongTensor(id_list_features_lengths),
        values=torch.cat(id_list_features_values),
    )
    return dense_features, sparse_features