class_docstr: The data, with weightings as actual standard deviations and/or
  covariances.
  
  Parameters
  ----------
  x : array_like
      Observed data for the independent variable of the regression
  y : array_like, optional
      If array-like, observed data for the dependent variable of the
      regression. A scalar input implies that the model to be used on
      the data is implicit.
  sx : array_like, optional
      Standard deviations of `x`.
      `sx` are standard deviations of `x` and are converted to weights by
      dividing 1.0 by their squares.
  sy : array_like, optional
      Standard deviations of `y`.
      `sy` are standard deviations of `y` and are converted to weights by
      dividing 1.0 by their squares.
  covx : array_like, optional
      Covariance of `x`
      `covx` is an array of covariance matrices of `x` and are converted to
      weights by performing a matrix inversion on each observation's
      covariance matrix.
  covy : array_like, optional
      Covariance of `y`
      `covy` is an array of covariance matrices and are converted to
      weights by performing a matrix inversion on each observation's
      covariance matrix.
  fix : array_like, optional
      The argument and member fix is the same as Data.fix and ODR.ifixx:
      It is an array of integers with the same shape as `x` that
      determines which input observations are treated as fixed. One can
      use a sequence of length m (the dimensionality of the input
      observations) to fix some dimensions for all observations. A value
      of 0 fixes the observation, a value > 0 makes it free.
  meta : dict, optional
      Free-form dictionary for metadata.
  
  Notes
  -----
  The weights `wd` and `we` are computed from provided values as follows:
  
  `sx` and `sy` are converted to weights by dividing 1.0 by their squares.
  For example, ``wd = 1./numpy.power(`sx`, 2)``.
  
  `covx` and `covy` are arrays of covariance matrices and are converted to
  weights by performing a matrix inversion on each observation's covariance
  matrix. For example, ``we[i] = numpy.linalg.inv(covy[i])``.
  
  These arguments follow the same structured argument conventions as wd and
  we only restricted by their natures: `sx` and `sy` can't be rank-3, but
  `covx` and `covy` can be.
  
  Only set *either* `sx` or `covx` (not both). Setting both will raise an
  exception. Same with `sy` and `covy`.
docstr: Convert covariance matrix(-ices) to weights.
          
docstr_tok: ["Convert","covariance","matrix","(","-ices",")","to","weights","."]
code: def _cov2wt(self, cov):
      """Convert covariance matrix(-ices) to weights."""
  
      from scipy.linalg import inv
  
      if len(cov.shape) == 2:
          return inv(cov)
      else:
          weights = numpy.zeros(cov.shape, float)
  
          for i in range(cov.shape[-1]):  # n
              weights[:, :, i] = inv(cov[:, :, i])
  
          return weights
  
imports: import os
  import numpy
  from warnings import warn
  from scipy.odr import __odrpack
label_desc: Use image node_id 3 for calling the RealData obj's underlying member method code with example usage: obj._cov2wt(cov) and returns: inv, weights