code: def _find_candidates_parameters_in_2d_grid(
      hist1: histograms.Histogram,
      hist2: histograms.Histogram,
      find_candidates_func1: Callable[
          [histograms.Histogram, int], Sequence[Number]
      ],
      find_candidates_func2: Callable[
          [histograms.Histogram, int], Sequence[Number]
      ],
      max_candidates: int,
  ) -> Tuple[Sequence[Number], Sequence[Number]]:
      """Finds candidates for 2 parameters.
  
      If we have 2 parameters to tune, then candidates for them form a 2
      dimensional grid. If for one parameter there is less than
      sqrt(max_candidates) candidates, we can add more candidates for the other
      parameter. This function implements this logic.
  
      Args:
          hist1: histogram of the distribution of the first parameter.
          hist2: histogram of the distribution of the second parameter.
          find_candidates_func1: function that given hist1 and maximum of
            candidates finds the candidates.
          find_candidates_func2: function that given hist2 and maximum of
            candidates finds the candidates.
          max_candidates: maximum number of the candidates to produce.
      Returns:
          Two sequences which represent pairs of candidates for parameters 1 and
            2. Sequences are of the same length and their lengths do not exceed
            max_candidates.
      """
  
      max_candidates_per_parameter = int(math.sqrt(max_candidates))
      param1_candidates = find_candidates_func1(
          hist1, max_candidates_per_parameter
      )
      param2_candidates = find_candidates_func2(
          hist2, max_candidates_per_parameter
      )
      param1_bounds, param2_bounds = [], []
  
      # if param1 or param2 has fewer candidates than requested then we can add
      # more candidates for the other parameter.
      if (
          len(param2_candidates) < max_candidates_per_parameter
          and len(param1_candidates) == max_candidates_per_parameter
      ):
          param1_candidates = find_candidates_func1(
              hist1, int(max_candidates / len(param2_candidates))
          )
      elif (
          len(param1_candidates) < max_candidates_per_parameter
          and len(param2_candidates) == max_candidates_per_parameter
      ):
          param2_candidates = find_candidates_func2(
              hist2, int(max_candidates / len(param1_candidates))
          )
  
      for param1 in param1_candidates:
          for param2 in param2_candidates:
              param1_bounds.append(param1)
              param2_bounds.append(param2)
  
      return param1_bounds, param2_bounds
  
imports: import logging
  import math
  from numbers import Number
  import pipeline_dp
  from pipeline_dp import dp_computations
  from pipeline_dp import pipeline_backend
  from pipeline_dp import input_validators
  from pipeline_dp.dataset_histograms import histograms
  import analysis
  from analysis import metrics
  from analysis import utility_analysis
  from analysis import dp_strategy_selector
  import dataclasses
  from dataclasses import dataclass
  from typing import Callable, List, Optional, Tuple, Union, Sequence
  from enum import Enum
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: _find_candidates_parameters_in_2d_grid(hist1, hist2, find_candidates_func1, find_candidates_func2, max_candidates) and returns: param1_bounds,  param2_bounds