docstr: Input Batch of Walks
          
docstr_tok: ["Input","Batch","of","Walks"]
code: def base_ego_generator(self):
      """Input Batch of Walks"""
      for walks in self.generator():
          # unique walk
          nodes = []
          for walk in walks:
              nodes.extend(walk)
  
          if self.config.sage_mode:  # GNN-based
              ego_graphs, uniq_nodes = ego_graph_sample(
                  self.graph,
                  nodes,
                  self.sample_num_list,
                  edge_types=self.edge_types,
              )
          else:  # walk-based
              ego_graphs = [EgoInfo(node_id=[nid]) for nid in nodes]
              uniq_nodes = nodes
  
          if len(self.config.slots) > 0:
              nfeat_dict = get_slots_feat(
                  self.graph, uniq_nodes, self.config.slots
              )
              for ego in ego_graphs:
                  ego.feature = make_slot_feat(
                      ego.node_id, self.config.slots, nfeat_dict
                  )
  
          start = 0
          egos = []
          for walk in walks:
              egos.append(ego_graphs[start : start + len(walk)])
              start += len(walk)
          yield egos
  
imports: import os
  import re
  import sys
  import time
  import warnings
  import numpy
  from collections import defaultdict
  import pgl
  from pgl.utils.logger import log
  from pgl.distributed import DistGraphClient, DistGraphServer
  from utils.config import prepare_config
  from datasets.helper import stream_shuffle_generator, AsynchronousGenerator
  from datasets.node import NodeGenerator
  import datasets.sampling
label_desc: Use image node_id 3 for calling the EgoGraphGenerator obj's underlying member method code with example usage: obj.base_ego_generator() without return types