class_docstr: Base class that returns the adjacency matrix for a network via
  the .create() method.
  
  Parameters
  -----------
  n_blocks : int (default=2)
      Number of blocks.  Returned matrix will be square with
      shape n_block_features * n_blocks.
  
  chain_blocks : bool (default=True)
      Apply random lattice structure to chain blocks together.
  
  seed : int
      Seed for np.random.RandomState seed. (default=1)
code: def to_covariance(self, precision, rescale=True):
      covariance = np.linalg.inv(precision)
  
      if rescale:
          return _rescale_to_unit_diagonals(covariance)
  
      return covariance
  
file_path: skggm/inverse_covariance/profiling/graphs.py
imports: import numpy
  import scipy
label_desc: Use image node_id 3 for calling the Graph obj's underlying member method code with example usage: obj.to_covariance(precision, rescale) and returns: covariance, _rescale_to_unit_diagonals