code: def predict(
      self, dataframe, params: Optional[Dict[str, Any]] = None
  ) -> pd.DataFrame:  # pylint: disable=unused-argument
      df_schema = dataframe.columns.values.tolist()
  
      if len(dataframe) > 1:
          raise MlflowException(
              f"The provided prediction pd.DataFrame contains {len(dataframe)} rows. "
              "Only 1 row should be supplied.",
              error_code=INVALID_PARAMETER_VALUE,
          )
  
      # Convert the configuration dataframe into a dictionary to simplify the
      # extraction of parameters passed to the sktime predcition methods.
      attrs = dataframe.to_dict(orient="index").get(0)
      predict_method = attrs.get("predict_method")
  
      if not predict_method:
          raise MlflowException(
              f"The provided prediction configuration pd.DataFrame columns ({df_schema}) do not "
              "contain the required column `predict_method` for specifying the prediction method.",
              error_code=INVALID_PARAMETER_VALUE,
          )
  
      if predict_method not in SUPPORTED_SKTIME_PREDICT_METHODS:
          raise MlflowException(
              "Invalid `predict_method` value."
              f"The supported prediction methods are {SUPPORTED_SKTIME_PREDICT_METHODS}",
              error_code=INVALID_PARAMETER_VALUE,
          )
  
      # For inference parameters 'fh', 'X', 'coverage', 'alpha', and 'cov'
      # the respective sktime default value is used if the value was not
      # provided in the configuration dataframe.
      fh = attrs.get("fh", None)
  
      # Any model that is trained with exogenous regressor elements will need
      # to provide `X` entries as a numpy ndarray to the predict method.
      X = attrs.get("X", None)
  
      # When the model is served via REST API the exogenous regressor must be
      # provided as a list to the configuration DataFrame to be JSON serializable.
      # Below we convert the list back to ndarray type as required by sktime
      # predict methods.
      if isinstance(X, list):
          X = np.array(X)
  
      # For illustration purposes only a subset of the available sktime prediction
      # methods is exposed. Additional methods (e.g. predict_proba) could be added
      # in a similar fashion.
      if predict_method == SKTIME_PREDICT:
          predictions = self.sktime_model.predict(fh=fh, X=X)
  
      if predict_method == SKTIME_PREDICT_INTERVAL:
          coverage = attrs.get("coverage", 0.9)
          predictions = self.sktime_model.predict_interval(
              fh=fh, X=X, coverage=coverage
          )
  
      if predict_method == SKTIME_PREDICT_QUANTILES:
          alpha = attrs.get("alpha", None)
          predictions = self.sktime_model.predict_quantiles(
              fh=fh, X=X, alpha=alpha
          )
  
      if predict_method == SKTIME_PREDICT_VAR:
          cov = attrs.get("cov", False)
          predictions = self.sktime_model.predict_var(
              fh=fh, X=X, cov=cov
          )
  
      # Methods predict_interval() and predict_quantiles() return a pandas
      # MultiIndex column structure. As MLflow signature inference does not
      # support MultiIndex column structure the columns must be flattened.
      if predict_method in [
          SKTIME_PREDICT_INTERVAL,
          SKTIME_PREDICT_QUANTILES,
      ]:
          predictions.columns = flatten_multiindex(predictions)
  
      return predictions
  
imports: import logging
  import os
  import pickle
  from typing import Any, Dict, Optional
  import flavor
  import numpy
  import pandas
  import sktime
  import yaml
  from sktime.utils.multiindex import flatten_multiindex
  import mlflow
  from mlflow import pyfunc
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import _save_example
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 2 for calling the _SktimeModelWrapper obj's underlying member method code with example usage: obj.predict(dataframe, params) and returns: predictions