code: def structure_score(model, data, scoring_method="bic", **kwargs):
      """
      Uses the standard model scoring methods to give a score for each structure.
      The score doesn't have very straight forward interpretebility but can be
      used to compare different models. A higher score represents a better fit.
      This method only needs the model structure to compute the score and parameters
      aren't required.
  
      Parameters
      ----------
      model: pgmpy.base.DAG or pgmpy.models.BayesianNetwork instance
          The model whose score needs to be computed.
  
      data: pd.DataFrame instance
          The dataset against which to score the model.
  
      scoring_method: str ( k2 | bdeu | bds | bic )
          The following four scoring methods are supported currently: 1) K2Score
          2) BDeuScore 3) BDsScore 4) BicScore
  
      kwargs: kwargs
          Any additional parameters that needs to be passed to the
          scoring method. Check pgmpy.estimators.StructureScore for details.
  
      Returns
      -------
      Model score: float
          A score value for the model.
  
      Examples
      --------
      >>> from pgmpy.utils import get_example_model
      >>> from pgmpy.metrics import structure_score
      >>> model = get_example_model('alarm')
      >>> data = model.simulate(int(1e4))
      >>> structure_score(model, data, scoring_method="bic")
      -106665.9383064447
      """
      from pgmpy.estimators import (
          BDeuScore,
          BDsScore,
          BicScore,
          K2Score,
      )
  
      supported_methods = {
          "k2": K2Score,
          "bdeu": BDeuScore,
          "bds": BDsScore,
          "bic": BicScore,
      }
  
      # Step 1: Test the inputs
      if not isinstance(model, (DAG, BayesianNetwork)):
          raise ValueError(
              f"model must be an instance of pgmpy.base.DAG or pgmpy.models.BayesianNetwork. Got {type(model)}"
          )
      elif not isinstance(data, pd.DataFrame):
          raise ValueError(
              f"data must be a pandas.DataFrame instance. Got {type(data)}"
          )
      elif set(model.nodes()) != set(data.columns):
          raise ValueError(
              f"Missing columns in data. Can't find values for the following variables: { set(model.nodes()) - set(data.columns) }"
          )
      elif (scoring_method not in supported_methods.keys()) and (
          not callable(scoring_method)
      ):
          raise ValueError(
              f"scoring method not supported and not a callable"
          )
  
      # Step 2: Compute the score and return
      return supported_methods[scoring_method](data, **kwargs).score(
          model
      )
  
imports: from itertools import combinations
  import pandas
  from sklearn.metrics import f1_score
  from pgmpy.base import DAG
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 3 for calling a global function with example usage: structure_score(model, data, scoring_method) and returns: supported_methods