class_docstr: Provider for parallel pattern across codebooks.
  This pattern provider is a special case of the delayed pattern with actually no delay,
  hence delays=repeat(0, n_q).
  
  Args:
      n_q (int): Number of codebooks.
      empty_initial (int): Prepend with N empty list of coordinates.
class_docstr_tok: ["Provider","for","parallel","pattern","across","codebooks",".","This","pattern","provider","is","a","special","case","of","the","delayed","pattern","with","actually","no","delay",",","hence","delays=repeat","(","0",",","n_q",")",".","Args",":","n_q","(","int",")",":","Number","of","codebooks",".","empty_initial","(","int",")",":","Prepend","with","N","empty","list","of","coordinates","."]
code: def __init__(self, n_q: int, empty_initial: int = 0):
      super().__init__(n_q, [0] * n_q, empty_initial=empty_initial)
  
imports: from collections import namedtuple
  from dataclasses import dataclass
  from functools import lru_cache
  import logging
  import typing
  from abc import ABC, abstractmethod
  import torch
label_desc: Use image node_id 1 to create a new ParallelPatternProvider object from inherited base classes: DelayedPatternProvider with example: obj = ParallelPatternProvider(n_q, empty_initial)