class_docstr: `ConcatedEncodedDs` abstracts over multiple encoded datasources (`EncodedDs`) as if they were a single entity.
class_docstr_tok: ["`","ConcatedEncodedDs","`","abstracts","over","multiple","encoded","datasources","(","`","EncodedDs","`",")","as","if","they","were","a","single","entity","."]
docstr: See `lightwood.data.encoded_ds.EncodedDs.__getitem__()`.
docstr_tok: ["See","`","lightwood.data.encoded_ds.EncodedDs.__getitem__","(",")","`","."]
code: def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      See `lightwood.data.encoded_ds.EncodedDs.__getitem__()`.
      """
      for ds_idx, length in enumerate(self.encoded_ds_lengths):
          if idx - length < 0:
              return self.encoded_ds_arr[ds_idx][idx]
          else:
              idx -= length
      raise StopIteration()
  
imports: import inspect
  from typing import List, Tuple, Dict
  import torch
  import numpy
  import pandas
  from torch.utils.data import Dataset
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 3 for calling the ConcatedEncodedDs obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: self