code: def determine_k(
      self, bin_counts, num_reference_embeddings, ref_includes_query
  ):
      self_count = int(ref_includes_query)
      max_bin_count = torch.max(bin_counts).item()
      if self.k == "max_bin_count":
          return max_bin_count - self_count
      if self.k is None:
          return num_reference_embeddings - self_count
      if self.k < max_bin_count:
          intersection = set(self.get_curr_metrics()).intersection(
              set({"r_precision", "mean_average_precision_at_r"})
          )
          if len(intersection) > 0:
              warning_str = f"\nWarning: You are computing {intersection}, but the value for k ({self.k})"
              warning_str += f" is less than the max bin count ({max_bin_count}) so the values for these metrics will be incorrect."
              warning_str += " To fix this, set k='max_bin_count'."
              warning_str += f"\nIf you're looking for MAP@{self.k} instead of MAP@R, then you should use 'mean_average_precision'"
              warning_str += " rather than mean_average_precision_at_r"
              c_f.LOGGER.warning(warning_str)
      return self.k
  
imports: import torch
  from sklearn.metrics import adjusted_mutual_info_score, normalized_mutual_info_score
  from .None import common_functions
  from .inference import FaissKMeans, FaissKNN
label_desc: Use image node_id 17 for calling the AccuracyCalculator obj's underlying member method code with example usage: obj.determine_k(bin_counts, num_reference_embeddings, ref_includes_query) and returns: self, unknown, unknown