class_docstr: Generates graph with certain number of nodes, edges
  and unique edges.
  
  Example
  -------
  >>> # Construct graph with 20 nodes, 10 edges and 4 unique edges types
  >>> generator = dummyGraphGenerator(20,10,4)
  >>> generator.get()
  {'train': array([['h', 'r', 'p'],
                   ['a', 't', 'd'],
                   ['a', 's', 'j'],
                   ['d', 'y', 'h'],
                   ['e', 'v', 'j'],
                   ['e', 'z', 'l'],
                   ['c', 'x', 'n']], dtype='<U32'),
   'valid': array([['l', 'x', 'j']], dtype='<U32'),
   'test':  array([['k', 's', 'c'],
                   ['i', 'u', 'o']], dtype='<U32')}
class_docstr_tok: ["Generates","graph","with","certain","number","of","nodes",",","edges","and","unique","edges",".","Example","--","--","--","-",">",">",">","#","Construct","graph","with","20","nodes",",","10","edges","and","4","unique","edges","types",">",">",">","generator","=","dummyGraphGenerator","(","20,10,4",")",">",">",">","generator.get","(",")","{","'train","'",":","array","(","[","[","'","h","'",",","'","r","'",",","'","p","'","]",",","[","'","a","'",",","'t","'",",","'d","'","]",",","[","'","a","'",",","'s","'",",","'","j","'","]",",","[","'d","'",",","'","y","'",",","'","h","'","]",",","[","'","e","'",",","'","v","'",",","'","j","'","]",",","[","'","e","'",",","'","z","'",",","'","l","'","]",",","[","'","c","'",",","'","x","'",",","'n","'","]","]",",","dtype=","'","<","U32","'",")",",","'valid","'",":","array","(","[","[","'","l","'",",","'","x","'",",","'","j","'","]","]",",","dtype=","'","<","U32","'",")",",","'test","'",":","array","(","[","[","'","k","'",",","'s","'",",","'","c","'","]",",","[","'","i","'",",","'","u","'",",","'","o","'","]","]",",","dtype=","'","<","U32","'",")","}"]
docstr: Delivers constructed graph.
  
  Returns
  -------
  X: dictionary in a format used in datasets with numpy arrays for train, validation 
  and test subsets
docstr_tok: ["Delivers","constructed","graph",".","Returns","--","--","--","-","X",":","dictionary","in","a","format","used","in","datasets","with","numpy","arrays","for","train",",","validation","and","test","subsets"]
code: def get(self, seed=None):
      """Delivers constructed graph.
  
      Returns
      -------
      X: dictionary in a format used in datasets with numpy arrays for train, validation
      and test subsets
      """
      nodes = self.get_unique(self.n_nodes, self.nodes)
      edges_labels = self.get_unique(self.n_unique_edges, self.edges)
      graph = self.generate_graph(nodes, edges_labels, seed=seed)
      size = len(graph)
  
      X = {
          "train": graph[: int(0.7 * size)],
          "valid": graph[int(0.7 * size) : int(0.8 * size)],
          "test": graph[int(0.8 * size) :],
      }
  
      return X
  
imports: import pytest
  from ampligraph.datasets.graph_partitioner import AbstractGraphPartitioner, RandomVerticesGraphPartitioner, RandomEdgesGraphPartitioner, SortedEdgesGraphPartitioner, NaiveGraphPartitioner, DoubleSortedEdgesGraphPartitioner, get_number_of_partitions, PARTITION_ALGO_REGISTRY, GraphDataLoader
  import numpy
  import pandas
  from scipy import special
  from itertools import permutations
  import os
label_desc: Use image node_id 4 for calling the dummyGraphGenerator obj's underlying member method code with example usage: obj.get(seed) and returns: X