code: def fast_logdet(A):
      """Compute logarithm of determinant of a square matrix.
  
      The (natural) logarithm of the determinant of a square matrix
      is returned if det(A) is non-negative and well defined.
      If the determinant is zero or negative returns -Inf.
  
      Equivalent to : np.log(np.det(A)) but more robust.
  
      Parameters
      ----------
      A : array_like of shape (n, n)
          The square matrix.
  
      Returns
      -------
      logdet : float
          When det(A) is strictly positive, log(det(A)) is returned.
          When det(A) is non-positive or not defined, then -inf is returned.
  
      See Also
      --------
      numpy.linalg.slogdet : Compute the sign and (natural) logarithm of the determinant
          of an array.
  
      Examples
      --------
      >>> import numpy as np
      >>> from sklearn.utils.extmath import fast_logdet
      >>> a = np.array([[5, 1], [2, 8]])
      >>> fast_logdet(a)
      3.6375861597263857
      """
      sign, ld = np.linalg.slogdet(A)
      if not sign > 0:
          return -np.inf
      return ld
  
imports: import warnings
  import numpy
  from scipy import linalg, sparse
  from .None import check_random_state
  from ._array_api import _is_numpy_namespace, get_namespace
  from ._logistic_sigmoid import _log_logistic_sigmoid
  from .sparsefuncs_fast import csr_row_norms
  from .validation import check_array
label_desc: Use image node_id 3 for calling a global function with example usage: fast_logdet(A) and returns: ld, unknown