code: def convert_to_model(val, keep_index=False):
      """Convert a model to a Model object.
  
      Parameters
      ----------
      val : function or Model object
          The model function or a Model object.
  
      keep_index : bool
          If True then the index values will be passed to the model function as the first argument.
          When this is False the feature names will be removed from the model object to avoid unnecessary warnings.
      """
      if isinstance(val, Model):
          out = val
      else:
          out = Model(val, None)
  
      # Fix for the sklearn warning
      # 'X does not have valid feature names, but <model> was fitted with feature names'
      if (
          not keep_index
      ):  # when using keep index, a dataframe with expected features names is expected to be passed
          f_self = getattr(out.f, "__self__", None)
          if f_self and hasattr(f_self, "feature_names_in_"):
              # Make a copy so that the feature names are not removed from the original model
              out = copy.deepcopy(out)
              out.f.__self__.feature_names_in_ = None
  
      return out
  
imports: import copy
  import numpy
  import pandas
  import scipy.sparse
  from sklearn.cluster import KMeans
  from sklearn.impute import SimpleImputer
label_desc: Use image node_id 5 for calling a global function with example usage: convert_to_model(val, keep_index) and returns: out