code: def configspace(self) -> ConfigurationSpace:
      # Build Configuration Space which defines all parameters and their ranges
      cs = ConfigurationSpace(seed=0)
  
      # First we create our hyperparameters
      kernel = Categorical(
          "kernel", ["linear", "poly", "rbf", "sigmoid"], default="poly"
      )
      C = Float("C", (0.001, 1000.0), default=1.0, log=True)
      shrinking = Categorical("shrinking", [True, False], default=True)
      degree = Integer("degree", (1, 5), default=3)
      coef = Float("coef0", (0.0, 10.0), default=0.0)
      gamma = Categorical("gamma", ["auto", "value"], default="auto")
      gamma_value = Float(
          "gamma_value", (0.0001, 8.0), default=1.0, log=True
      )
  
      # Then we create dependencies
      use_degree = InCondition(
          child=degree, parent=kernel, values=["poly"]
      )
      use_coef = InCondition(
          child=coef, parent=kernel, values=["poly", "sigmoid"]
      )
      use_gamma = InCondition(
          child=gamma, parent=kernel, values=["rbf", "poly", "sigmoid"]
      )
      use_gamma_value = InCondition(
          child=gamma_value, parent=gamma, values=["value"]
      )
  
      # Add hyperparameters and conditions to our configspace
      cs.add_hyperparameters(
          [kernel, C, shrinking, degree, coef, gamma, gamma_value]
      )
      cs.add_conditions(
          [use_degree, use_coef, use_gamma, use_gamma_value]
      )
  
      return cs
  
file_path: SMAC3/benchmark/src/models/svm.py
imports: from __future__ import annotations
  import numpy
  from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Float, Integer
  from ConfigSpace.conditions import InCondition
  from sklearn import svm
  from sklearn.model_selection import cross_val_score
  from src.datasets.dataset import Dataset
  from src.models.model import Model
label_desc: Use image node_id 2 for calling the SVMModel obj's underlying member method code with example usage: obj.configspace() and returns: cs