class_docstr: Class to make diffusions for a given graph.
  
  Args:
      diffusion_number (int): Number of diffusions
      diffusion_cover (int): Number of nodes in diffusion.
class_docstr_tok: ["Class","to","make","diffusions","for","a","given","graph",".","Args",":","diffusion_number","(","int",")",":","Number","of","diffusions","diffusion_cover","(","int",")",":","Number","of","nodes","in","diffusion","."]
docstr: Running diffusions from every node.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to run diffusions on.
docstr_tok: ["Running","diffusions","from","every","node",".","Arg","types",":","*","*","*","graph","*","*","*","(","NetworkX","graph",")","*","-","The","graph","to","run","diffusions","on","."]
code: def do_diffusions(self, graph: nx.classes.graph.Graph):
      """
      Running diffusions from every node.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to run diffusions on.
      """
      self.graph = graph
      self.diffusions = []
      for _ in range(self.diffusion_number):
          for node in self.graph.nodes():
              diffusion_sequence = self._run_diffusion_process(node)
              self.diffusions.append(diffusion_sequence)
  
imports: import random
  import networkx
label_desc: Use image node_id 3 for calling the EulerianDiffuser obj's underlying member method code with example usage: obj.do_diffusions(graph) without return types