code: def _compute_absolute_step(rel_step, x0, f0, method):
      """
      Computes an absolute step from a relative step for finite difference
      calculation.
  
      Parameters
      ----------
      rel_step: None or array-like
          Relative step for the finite difference calculation
      x0 : np.ndarray
          Parameter vector
      f0 : np.ndarray or scalar
      method : {'2-point', '3-point', 'cs'}
  
      Returns
      -------
      h : float
          The absolute step size
  
      Notes
      -----
      `h` will always be np.float64. However, if `x0` or `f0` are
      smaller floating point dtypes (e.g. np.float32), then the absolute
      step size will be calculated from the smallest floating point size.
      """
      if rel_step is None:
          rel_step = _eps_for_method(x0.dtype, f0.dtype, method)
      sign_x0 = (x0 >= 0).astype(float) * 2 - 1
      return rel_step * sign_x0 * np.maximum(1.0, np.abs(x0))
  
file_path: catboost/contrib/python/scipy/py3/scipy/optimize/_numdiff.py
imports: import functools
  import numpy
  from numpy.linalg import norm
  from scipy.sparse.linalg import LinearOperator
  from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
  from ._group_columns import group_dense, group_sparse
label_desc: Use image node_id 3 for calling a global function with example usage: _compute_absolute_step(rel_step, x0, f0, method) and returns: unknown