code: def _compute_gamma_sigma(self, gamma, X):
      if isinstance(gamma, str):
          if gamma == "scale":
              if sp.issparse(X):
                  # var = E[X^2] - E[X]^2
                  X_sc = (X.multiply(X)).mean() - (X.mean()) ** 2
              else:
                  X_sc = X.var()
              _gamma = 1.0 / (X.shape[1] * X_sc) if X_sc != 0 else 1.0
          elif gamma == "auto":
              _gamma = 1.0 / X.shape[1]
          else:
              raise ValueError(
                  "When 'gamma' is a string, it should be either 'scale' or "
                  "'auto'. Got '{}' instead.".format(gamma)
              )
      else:
          if sklearn_check_version("1.1") and not sklearn_check_version(
              "1.2"
          ):
              if isinstance(gamma, Real):
                  if gamma <= 0:
                      msg = (
                          f"gamma value must be > 0; {gamma!r} is invalid. Use"
                          " a positive number or use 'auto' to set gamma to a"
                          " value of 1 / n_features."
                      )
                      raise ValueError(msg)
                  _gamma = gamma
              else:
                  msg = (
                      "The gamma value should be set to 'scale', 'auto' or a"
                      f" positive float value. {gamma!r} is not a valid option"
                  )
                  raise ValueError(msg)
          else:
              _gamma = gamma
      return _gamma, np.sqrt(0.5 / _gamma)
  
imports: from abc import ABCMeta, abstractmethod
  from enum import Enum
  from numbers import Number, Real
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import sklearn_check_version
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin, RegressorMixin
  from ..common._policy import _get_policy
  from ..datatypes import from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _column_or_1d, _validate_targets
label_desc: Use image node_id 2 for calling the BaseSVM obj's underlying member method code with example usage: obj._compute_gamma_sigma(gamma, X) and returns: _gamma,  np