code: def get_params(self, deep=True):
      params = self.__dict__
  
      # if kwargs is a dict, update params accordingly
      if hasattr(self, "kwargs") and isinstance(self.kwargs, dict):
          params.update(self.kwargs)
  
      # take random_state into account only if it's an integer
      if isinstance(params["random_state"], int):
          ivy.seed(seed_value=params["random_state"])
  
      return params
  
file_path: ivy/ivy/functional/frontends/xgboost/sklearn.py
imports: import ivy
  from ivy.functional.frontends.sklearn.base import BaseEstimator
  from ivy.functional.frontends.sklearn.base import ClassifierMixin
  from .training import train
  from .core import Booster
label_desc: Use image node_id 4 for calling the XGBModel obj's underlying member method code with example usage: obj.get_params(deep) and returns: params