code: def from_formula(
      cls, formula, data, subset=None, drop_cols=None, *args, **kwargs
  ):
      try:
          groups = kwargs["groups"]
          del kwargs["groups"]
      except KeyError:
          raise ValueError("'groups' is a required argument")
  
      if isinstance(groups, str):
          groups = data[groups]
  
      if "0+" not in formula.replace(" ", ""):
          warnings.warn(
              "Conditional models should not include an intercept"
          )
  
      model = super().from_formula(
          formula, data=data, groups=groups, *args, **kwargs
      )
  
      return model
  
imports: import numpy
  import statsmodels.base.model
  import statsmodels.regression.linear_model
  import statsmodels.base.wrapper
  from statsmodels.discrete.discrete_model import MultinomialResults, MultinomialResultsWrapper
  import collections
  import warnings
  import itertools
label_desc: Use image node_id 5 for calling the _ConditionalModel obj's underlying member method code with example usage: obj.from_formula(cls, formula, data, subset, drop_cols) and returns: model