class_docstr: Integration with the Nixtla TimeGPT models for
  zero-shot time series forecasting.
class_docstr_tok: ["Integration","with","the","Nixtla","TimeGPT","models","for","zero-shot","time","series","forecasting","."]
code: def describe(self, attribute=None):
      model_args = self.model_storage.json_get("model_args")
  
      if attribute == "model":
          df = pd.DataFrame(
              {
                  "frequency": [
                      model_args["freq"]
                      if model_args["freq"]
                      else "automatic"
                  ]
              }
          )
          return df
  
      elif attribute == "features":
          df = pd.DataFrame(
              {
                  "order by": [model_args["order_by"]],
                  "target": model_args["target"],
              }
          )
          if model_args["group_by"]:
              df["group by"] = [model_args["group_by"]]
          return df
  
      elif attribute == "info":
          outputs = model_args["target"]
          inputs = [model_args["target"], model_args["order_by"]]
          if model_args["group_by"]:
              inputs.append(model_args["group_by"])
          return pd.DataFrame({"output": outputs, "input": [inputs]})
  
      else:
          tables = ["info", "features", "model"]
          return pd.DataFrame(tables, columns=["tables"])
  
file_path: mindsdb/mindsdb/integrations/handlers/timegpt_handler/timegpt_handler.py
imports: from typing import Optional, Dict
  import pandas
  from nixtlats import TimeGPT
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.integrations.utilities.handler_utils import get_api_key
  from mindsdb.integrations.utilities.time_series_utils import get_results_from_nixtla_df
label_desc: Use image node_id 3 for calling the TimeGPTHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: df, df, pd, pd