code: def stabilize(K, jitter=JITTER_DEFAULT):
      R"""
      Adds small diagonal to a covariance matrix.
  
      Often the matrices calculated from covariance functions, `K = cov_func(X)`
      do not appear numerically to be positive semi-definite.  Adding a small
      correction, `jitter`, to the diagonal is usually enough to fix this.
  
      Parameters
      ----------
      K: array-like
          A square covariance or kernel matrix.
      jitter: float
          A small constant.
      """
      return K + jitter * pt.identity_like(K)
  
file_path: pymc/pymc/gp/util.py
imports: import warnings
  import numpy
  import pytensor.tensor
  from pytensor.compile import SharedVariable
  from pytensor.graph import ancestors
  from pytensor.tensor.variable import TensorConstant
  from scipy.cluster.vq import kmeans
  from pymc.distributions.distribution import Distribution
  from pymc.model import modelcontext
  from pymc.pytensorf import compile_pymc
label_desc: Use image node_id 2 for calling a global function with example usage: stabilize(K, jitter) and returns: unknown