class_docstr: Returns the adjacency matrix for Erdos-Renyi network via .create().
  
  Parameters
  -----------
  spd_low : float (0, 1)
      Equivalent to make_sparse_spd_matrix `smallest_coef`
  
  spd_high : float (0, 1)
      Equivalent to make_sparse_spd_matrix `largest_coef`
  
  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, spd_low=0.7, spd_high=0.7, **kwargs):
      self.spd_low = spd_low
      self.spd_high = spd_high
      super(ErdosRenyiGraph, self).__init__(**kwargs)
  
file_path: skggm/inverse_covariance/profiling/erdos_renyi_graph.py
imports: from __future__ import absolute_import
  import numpy
  from sklearn.datasets import make_sparse_spd_matrix
  from .graphs import Graph
label_desc: Use image node_id 1 to create a new ErdosRenyiGraph object from inherited base classes: Graph with example: obj = ErdosRenyiGraph(spd_low, spd_high)