code: def generate_sequential_graphs(bs=5, num_nodes=10):
      start_index = list(range(0, num_nodes - 1, 1))
      end_index = list(range(1, num_nodes, 1))
      graphs = []
      node_features = []
      node_features_2 = []
      edge_features = []
      edge_features_2 = []
      for i in range(bs):
          g = GraphData()
          g.add_nodes(num_nodes)
          g.add_edges(src=start_index, tgt=end_index)
          node_feat = torch.FloatTensor([i + 1] * num_nodes)
          node_feat_2 = torch.FloatTensor([i] * num_nodes)
          g.node_features["node_feat"] = node_feat
          node_features.append(node_feat)
          node_features_2.append(node_feat_2)
  
          edge_feat = torch.FloatTensor(
              [(i + 1) * 10] * (num_nodes - 1)
          )
          edge_feat_2 = torch.FloatTensor([i * 10] * (num_nodes - 1))
          g.edge_features["edge_feat"] = edge_feat
          edge_features.append(edge_feat)
          edge_features_2.append(edge_feat_2)
          graphs.append(g)
      return (
          edge_features,
          edge_features_2,
          graphs,
          node_features,
          node_features_2,
      )
  
imports: import gc
  import time
  import matplotlib.pyplot
  import torch
  import torch.nn
  import pytest
  from ...data.data import GraphData, from_batch, from_dgl, to_batch
  from ...data.utils import EdgeNotFoundException, SizeMismatchException
label_desc: Use image node_id 13 for calling a global function with example usage: generate_sequential_graphs(bs, num_nodes) and returns: edge_features,  edge_features_2,  graphs,  node_features,  node_features_2