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_precision(self, adjacency, weighted=True, rescale=True):
      if weighted:
          dd_adj = _to_diagonally_dominant_weighted(adjacency)
      else:
          dd_adj = _to_diagonally_dominant(adjacency)
  
      if rescale:
          return _rescale_to_unit_diagonals(dd_adj)
  
      return dd_adj
  
file_path: skggm/inverse_covariance/profiling/graphs.py
imports: import numpy
  import scipy
label_desc: Use image node_id 2 for calling the Graph obj's underlying member method code with example usage: obj.to_precision(adjacency, weighted, rescale) and returns: dd_adj, _rescale_to_unit_diagonals