class_docstr: Integration with the Nixtla NeuralForecast library for
  time series forecasting with neural networks.
class_docstr_tok: ["Integration","with","the","Nixtla","NeuralForecast","library","for","time","series","forecasting","with","neural","networks","."]
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", "hierarchy"]
              }
          )
  
      elif attribute == "features":
          return pd.DataFrame(
              {
                  "ds": [model_args["order_by"]],
                  "y": model_args["target"],
                  "unique_id": [model_args["group_by"]],
                  "exog_vars": [model_args["exog_vars"]],
              }
          )
  
      elif attribute == "info":
          outputs = model_args["target"]
          inputs = [
              model_args["target"],
              model_args["order_by"],
              model_args["group_by"],
          ] + model_args["exog_vars"]
          accuracies = [
              (model, acc)
              for model, acc in model_args.get("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/neuralforecast_handler/neuralforecast_handler.py
imports: from sklearn.metrics import r2_score
  import dill
  import pandas
  import tempfile
  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_model_accuracy_dict, get_hierarchy_from_df, reconcile_forecasts
  from neuralforecast import NeuralForecast
  from neuralforecast.models import NHITS
  from neuralforecast.auto import AutoNHITS
  from ray.tune.search.hyperopt import HyperOptSearch
label_desc: Use image node_id 3 for calling the NeuralForecastHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: pd, pd, pd, pd