code: def numerical_derivative(func, x, eps):
      """Helper function for numerical (first) derivatives."""
      # For numerical derivatives, see
      # https://en.wikipedia.org/wiki/Numerical_differentiation
      # https://en.wikipedia.org/wiki/Finite_difference_coefficient
      # We use central finite differences of accuracy 4.
      h = np.full_like(x, fill_value=eps)
      f_minus_2h = func(x - 2 * h)
      f_minus_1h = func(x - h)
      f_plus_1h = func(x + h)
      f_plus_2h = func(x + 2 * h)
      return (
          -f_plus_2h + 8 * f_plus_1h - 8 * f_minus_1h + f_minus_2h
      ) / (12.0 * eps)
  
imports: import pickle
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_equal
  from pytest import approx
  from scipy.optimize import LinearConstraint, minimize, minimize_scalar, newton
  from scipy.special import logsumexp
  from sklearn._loss.link import IdentityLink, _inclusive_low_high
  from sklearn._loss.loss import _LOSSES, AbsoluteError, BaseLoss, HalfBinomialLoss, HalfGammaLoss, HalfMultinomialLoss, HalfPoissonLoss, HalfSquaredError, HalfTweedieLoss, HalfTweedieLossIdentity, HuberLoss, PinballLoss
  from sklearn.utils import _IS_WASM, assert_all_finite
  from sklearn.utils._testing import create_memmap_backed_data, skip_if_32bit
label_desc: Use image node_id 3 for calling a global function with example usage: numerical_derivative(func, x, eps) and returns: unknown