class_docstr: Performs fast :math:`k`-nearest neighbor search (:math:`k`-NN) based on
  the :math:`L_2` metric via the :obj:`faiss` library.
  
  Args:
      emb (torch.Tensor, optional): The data points to add.
          (default: :obj:`None`)
class_docstr_tok: ["Performs","fast",":","math",":","`","k","`","-nearest","neighbor","search","(",":","math",":","`","k","`","-NN",")","based","on","the",":","math",":","`","L_2","`","metric","via","the",":","obj",":","`","faiss","`","library",".","Args",":","emb","(","torch.Tensor",",","optional",")",":","The","data","points","to","add",".","(","default",":",":","obj",":","`","None","`",")"]
code: def _create_index(self, channels: int):
      import faiss
  
      return faiss.IndexFlatL2(channels)
  
imports: import warnings
  from typing import NamedTuple, Optional
  import torch
  from torch import Tensor
  from torch_geometric.utils import cumsum, degree, to_dense_batch
label_desc: Use image node_id 2 for calling the L2KNNIndex obj's underlying member method code with example usage: obj._create_index(channels) and returns: faiss