code: def read_ba2motif_data(folder: str, prefix):
      with open(os.path.join(folder, f"{prefix}.pkl"), "rb") as f:
          dense_edges, node_features, graph_labels = pickle.load(f)
  
      data_list = []
      for graph_idx in range(dense_edges.shape[0]):
          edge_index = dense_to_sparse(
              torch.from_numpy(dense_edges[graph_idx])
          )[0]
          data_list.append(
              Data(
                  x=torch.from_numpy(node_features[graph_idx]).float(),
                  edge_index=edge_index,
                  y=torch.from_numpy(
                      np.where(graph_labels[graph_idx])[0]
                  ),
              )
          )
      return data_list
  
imports: import os
  import torch
  import pickle
  import numpy
  import os.path
  import tqdm
  from torch_geometric.utils import dense_to_sparse
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.data.dataset import files_exist
  import shutil
label_desc: Use image node_id 1 for calling a global function with example usage: read_ba2motif_data(folder, prefix) and returns: data_list