docstr: Check if the order of the nodes is the same order as `sm.nodes`, which in turn is the same order as the
  adjacency matrix.
  
  To do so, we create graphs with degree in {0,1} by doing permutations on identity.
  The edge values are always 100 and the noise is 1, so we expect `edge_from` < `edge_to` in absolute value
  almost every time.
docstr_tok: ["Check","if","the","order","of","the","nodes","is","the","same","order","as","`","sm.nodes","`",",","which","in","turn","is","the","same","order","as","the","adjacency","matrix",".","To","do","so",",","we","create","graphs","with","degree","in","{","0,1","}","by","doing","permutations","on","identity",".","The","edge","values","are","always","100","and","the","noise","is","1",",","so","we","expect","`","edge_from","`","<","`","edge_to","`","in","absolute","value","almost","every","time","."]
code: def test_order_is_correct(self, graph_gen, num_nodes, seed):
      """
      Check if the order of the nodes is the same order as `sm.nodes`, which in turn is the same order as the
      adjacency matrix.
  
      To do so, we create graphs with degree in {0,1} by doing permutations on identity.
      The edge values are always 100 and the noise is 1, so we expect `edge_from` < `edge_to` in absolute value
      almost every time.
      """
      sm = graph_gen(num_nodes=num_nodes, seed=seed, weight=100)
      nodes = sm.nodes()
      node_seq = {node: ix for ix, node in enumerate(sm.nodes())}
  
      data = generate_continuous_data(
          sm,
          n_samples=10000,
          distribution="normal",
          seed=seed,
          noise_scale=1.0,
          intercept=False,
      )
  
      assert (
          data[:, node_seq["aa"]].std() < data[:, node_seq["ab"]].std()
      )
  
      tol = 0.15
      # for gaussian distribution: var=0 iff independent:
      for node in nodes:
          if node == "aa":
              continue
          if node == "ab":
              assert not np.isclose(
                  np.corrcoef(
                      data[:, [node_seq["aa"], node_seq["ab"]]].T
                  )[0, 1],
                  0,
                  atol=tol,
              )
          else:
              assert np.isclose(
                  np.corrcoef(
                      data[:, [node_seq["aa"], node_seq[node]]].T
                  )[0, 1],
                  0,
                  atol=tol,
              )
  
imports: import re
  import string
  from itertools import product
  import numpy
  import pandas
  import pytest
  from scipy import stats
  from sklearn.gaussian_process.kernels import RBF
  from causalnex.structure import StructureModel
  from causalnex.structure.data_generators import gen_stationary_dyn_net_and_df, generate_binary_data, generate_binary_dataframe, generate_categorical_dataframe, generate_continuous_data, generate_continuous_dataframe, generate_count_dataframe, generate_dataframe_dynamic, generate_structure, generate_structure_dynamic
  from tests.structure.data_generators.test_core import calculate_proba
label_desc: Use image node_id 11 for calling the TestGenerateContinuousData obj's underlying member method code with example usage: obj.test_order_is_correct(graph_gen, num_nodes, seed) without return types