code: def matrix_sqrt(
      mat, inverse=False, full=False, nullspace=False, threshold=1e-15
  ):
      """matrix square root for symmetric matrices
  
      Usage is for decomposing a covariance function S into a square root R
      such that
  
          R' R = S if inverse is False, or
          R' R = pinv(S) if inverse is True
  
      Parameters
      ----------
      mat : array_like, 2-d square
          symmetric square matrix for which square root or inverse square
          root is computed.
          There is no checking for whether the matrix is symmetric.
          A warning is issued if some singular values are negative, i.e.
          below the negative of the threshold.
      inverse : bool
          If False (default), then the matrix square root is returned.
          If inverse is True, then the matrix square root of the inverse
          matrix is returned.
      full : bool
          If full is False (default, then the square root has reduce number
          of rows if the matrix is singular, i.e. has singular values below
          the threshold.
      nullspace : bool
          If nullspace is true, then the matrix square root of the null space
          of the matrix is returned.
      threshold : float
          Singular values below the threshold are dropped.
  
      Returns
      -------
      msqrt : ndarray
          matrix square root or square root of inverse matrix.
      """
      # see also scipy.linalg null_space
      u, s, v = np.linalg.svd(mat)
      if np.any(s < -threshold):
          import warnings
  
          warnings.warn("some singular values are negative")
  
      if not nullspace:
          mask = s > threshold
          s[s < threshold] = 0
      else:
          mask = s < threshold
          s[s > threshold] = 0
  
      sqrt_s = np.sqrt(s[mask])
      if inverse:
          sqrt_s = 1 / np.sqrt(s[mask])
  
      if full:
          b = np.dot(u[:, mask], np.dot(np.diag(sqrt_s), v[mask]))
      else:
          b = np.dot(np.diag(sqrt_s), v[mask])
      return b
  
imports: import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: matrix_sqrt(mat, inverse, full, nullspace, threshold) and returns: b