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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code: def __init__(self, random_sign=False, low=0.7, high=0.7, **kwargs):
      self.random_sign = random_sign
      self.low = low
      self.high = high
      super(LatticeGraph, self).__init__(**kwargs)
  
imports: from __future__ import absolute_import
  from .graphs import Graph, lattice
label_desc: Use image node_id 1 to create a new LatticeGraph object from inherited base classes: Graph with example: obj = LatticeGraph(random_sign, low, high)