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 __init__(self, n_blocks=2, chain_blocks=True, seed=1):
      self.n_blocks = n_blocks
      self.chain_blocks = chain_blocks
      self.seed = seed
      self.prng = np.random.RandomState(self.seed)
  
      if n_blocks == 1 and chain_blocks:
          raise ValueError(
              "Requires chain_blocks=False when n_blocks=1."
          )
          return
  
file_path: skggm/inverse_covariance/profiling/graphs.py
imports: import numpy
  import scipy
label_desc: Use image node_id 1 to create a new Graph object from inherited base classes: object with example: obj = Graph(n_blocks, chain_blocks, seed)