code: def load_node_csv(path, index_col, encoders=None, **kwargs):
      df = pd.read_csv(path, index_col=index_col, **kwargs)
      mapping = {index: i for i, index in enumerate(df.index.unique())}
  
      x = None
      if encoders is not None:
          xs = [encoder(df[col]) for col, encoder in encoders.items()]
          x = torch.cat(xs, dim=-1)
  
      return x, mapping
  
file_path: pytorch_geometric/examples/hetero/load_csv.py
imports: import os.path
  import pandas
  import torch
  from sentence_transformers import SentenceTransformer
  from torch_geometric.data import HeteroData, download_url, extract_zip
  from torch_geometric.transforms import RandomLinkSplit, ToUndirected
label_desc: Use image node_id 1 for calling a global function with example usage: load_node_csv(path, index_col, encoders) and returns: x,  mapping