class_docstr: Integration with the Nixtla StatsForecast library for
  time series forecasting with classical methods.
class_docstr_tok: ["Integration","with","the","Nixtla","StatsForecast","library","for","time","series","forecasting","with","classical","methods","."]
code: def describe(self, attribute=None):
      model_args = self.model_storage.json_get("model_args")
  
      if attribute == "model":
          return pd.DataFrame(
              {
                  k: [model_args[k]]
                  for k in [
                      "model_name",
                      "frequency",
                      "season_length",
                      "hierarchy",
                  ]
              }
          )
  
      elif attribute == "features":
          return pd.DataFrame(
              {
                  "ds": [model_args["order_by"]],
                  "y": model_args["target"],
                  "unique_id": [model_args["group_by"]],
              }
          )
  
      elif attribute == "info":
          outputs = model_args["target"]
          inputs = [
              model_args["target"],
              model_args["order_by"],
              model_args["group_by"],
          ]
          accuracies = [
              (model, acc)
              for model, acc in model_args["accuracies"].items()
          ]
          return pd.DataFrame(
              {
                  "accuracies": [accuracies],
                  "outputs": outputs,
                  "inputs": [inputs],
              }
          )
  
      else:
          tables = ["info", "features", "model"]
          return pd.DataFrame(tables, columns=["tables"])
  
file_path: mindsdb/mindsdb/integrations/handlers/statsforecast_handler/statsforecast_handler.py
imports: import pandas
  import dill
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.integrations.utilities.time_series_utils import transform_to_nixtla_df, get_results_from_nixtla_df, infer_frequency, get_best_model_from_results_df, get_model_accuracy_dict, reconcile_forecasts, get_hierarchy_from_df
  from sklearn.metrics import r2_score
  from statsforecast import StatsForecast
  from statsforecast.models import AutoARIMA, AutoCES, AutoETS, AutoTheta
label_desc: Use image node_id 3 for calling the StatsForecastHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: pd, pd, pd, pd