code: def read_ego(files: List[str], name: str) -> List[EgoData]:
      import pandas as pd
  
      all_featnames = []
      files = [
          x
          for x in files
          if x.split(".")[-1]
          in ["circles", "edges", "egofeat", "feat", "featnames"]
      ]
      for i in range(4, len(files), 5):
          featnames_file = files[i]
          with fsspec.open(featnames_file, "r") as f:
              featnames = f.read().split("\n")[:-1]
              featnames = [
                  " ".join(x.split(" ")[1:]) for x in featnames
              ]
              all_featnames += featnames
      all_featnames = sorted(list(set(all_featnames)))
      all_featnames_dict = {
          key: i for i, key in enumerate(all_featnames)
      }
  
      data_list = []
      for i in range(0, len(files), 5):
          circles_file = files[i]
          edges_file = files[i + 1]
          egofeat_file = files[i + 2]
          feat_file = files[i + 3]
          featnames_file = files[i + 4]
  
          x = None
          if name != "gplus":  # Don't read node features on g-plus:
              x_ego = pd.read_csv(
                  egofeat_file, sep=" ", header=None, dtype=np.float32
              )
              x_ego = torch.from_numpy(x_ego.values)
  
              x = pd.read_csv(
                  feat_file, sep=" ", header=None, dtype=np.float32
              )
              x = torch.from_numpy(x.values)[:, 1:]
  
              x_all = torch.cat([x, x_ego], dim=0)
  
              # Reorder `x` according to `featnames` ordering.
              x_all = torch.zeros(x.size(0), len(all_featnames))
              with fsspec.open(featnames_file, "r") as f:
                  featnames = f.read().split("\n")[:-1]
                  featnames = [
                      " ".join(x.split(" ")[1:]) for x in featnames
                  ]
              indices = [
                  all_featnames_dict[featname] for featname in featnames
              ]
              x_all[:, torch.tensor(indices)] = x
              x = x_all
  
          idx = pd.read_csv(
              feat_file, sep=" ", header=None, dtype=str, usecols=[0]
          ).squeeze()
  
          idx_assoc: Dict[str, int] = {}
          for i, j in enumerate(idx):
              idx_assoc[j] = i
  
          circles: List[int] = []
          circles_batch: List[int] = []
          with fsspec.open(circles_file, "r") as f:
              for i, line in enumerate(f.read().split("\n")[:-1]):
                  circle_indices = [
                      idx_assoc[c] for c in line.split()[1:]
                  ]
                  circles += circle_indices
                  circles_batch += [i] * len(circle_indices)
          circle = torch.tensor(circles)
          circle_batch = torch.tensor(circles_batch)
  
          try:
              row = pd.read_csv(
                  edges_file,
                  sep=" ",
                  header=None,
                  dtype=str,
                  usecols=[0],
              ).squeeze()
              col = pd.read_csv(
                  edges_file,
                  sep=" ",
                  header=None,
                  dtype=str,
                  usecols=[1],
              ).squeeze()
          except Exception:
              continue
  
          row = torch.tensor([idx_assoc[i] for i in row])
          col = torch.tensor([idx_assoc[i] for i in col])
  
          N = max(int(row.max()), int(col.max())) + 2
          N = x.size(0) if x is not None else N
  
          row_ego = torch.full((N - 1,), N - 1, dtype=torch.long)
          col_ego = torch.arange(N - 1)
  
          # Ego node should be connected to every other node.
          row = torch.cat([row, row_ego, col_ego], dim=0)
          col = torch.cat([col, col_ego, row_ego], dim=0)
          edge_index = torch.stack([row, col], dim=0)
          edge_index = coalesce(edge_index, num_nodes=N)
  
          data = EgoData(
              x=x,
              edge_index=edge_index,
              circle=circle,
              circle_batch=circle_batch,
          )
  
          data_list.append(data)
  
      return data_list
  
imports: import os
  import os.path
  from typing import Any, Callable, Dict, List, Optional, Union
  import fsspec
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.io import fs
  from torch_geometric.utils import coalesce
label_desc: Use image node_id 1 for calling a global function with example usage: read_ego(files, name) and returns: data_list