class_docstr: Returns the adjacency matrix for a lattice/banded network via .create().
  
  The graph can be made fully connected using chaining assumption when
  chain_blocks=True (default).
  
  Parameters
  -----------
  random sign : bool (default=False)
      Randomly modulate each entry by 1 or -1 with probability of 1/2.
  
  low : float (0, 1) (default=0.3)
      Lower bound for np.random.RandomState.uniform before normalization.
  
  high : float (0, 1) > low (default=0.7)
      Upper bound for np.random.RandomState.uniform before normalization.
  
  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)
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docstr: Build a new graph.
  
  Doc for ".create(n_features, alpha)"
  
  Parameters
  -----------
  n_features : int
  
  alpha : float (0,1)
      The complexity / sparsity factor.
  
      Each graph will have a minimum of
  
          n_blocks * ceil(alpha * n_block_features)
  
          where
  
          n_block_features = floor(n_features / self.n_blocks)
  
      edges and exactly this amount if chain_blocks=False.
  
  Returns
  -----------
  (n_features, n_features) matrices: covariance, precision, adjacency
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code: def prototype_adjacency(self, n_block_features, alpha):
      """Build a new graph.
  
      Doc for ".create(n_features, alpha)"
  
      Parameters
      -----------
      n_features : int
  
      alpha : float (0,1)
          The complexity / sparsity factor.
  
          Each graph will have a minimum of
  
              n_blocks * ceil(alpha * n_block_features)
  
              where
  
              n_block_features = floor(n_features / self.n_blocks)
  
          edges and exactly this amount if chain_blocks=False.
  
      Returns
      -----------
      (n_features, n_features) matrices: covariance, precision, adjacency
      """
      return lattice(
          self.prng,
          n_block_features,
          alpha,
          random_sign=self.random_sign,
          low=self.low,
          high=self.high,
      )
  
imports: from __future__ import absolute_import
  from .graphs import Graph, lattice
label_desc: Use image node_id 2 for calling the LatticeGraph obj's underlying member method code with example usage: obj.prototype_adjacency(n_block_features, alpha) and returns: lattice