docstr: Compute a thresholds array for all classes based on `self.thes` and `self.lower_thres`.
  Args:
      common_class_names (Optional[list of strs]): a list of common class names.
docstr_tok: ["Compute","a","thresholds","array","for","all","classes","based","on","`","self.thes","`","and","`","self.lower_thres","`",".","Args",":","common_class_names","(","Optional","[","list","of","strs","]",")",":","a","list","of","common","class","names","."]
code: def _get_thres_array(self, common_class_names=None):
      """
      Compute a thresholds array for all classes based on `self.thes` and `self.lower_thres`.
      Args:
          common_class_names (Optional[list of strs]): a list of common class names.
      """
      common_class_ids = []
      if common_class_names is not None:
          common_classes = set(common_class_names)
  
          for i, name in enumerate(self.class_names):
              if name in common_classes:
                  common_class_ids.append(i)
      else:
          common_class_ids = list(range(self.num_classes))
  
      thres_array = np.full(
          shape=(self.num_classes,), fill_value=self.lower_thres
      )
      thres_array[common_class_ids] = self.thres
      self.thres = torch.from_numpy(thres_array)
  
imports: import itertools
  import logging
  import numpy
  import matplotlib.pyplot
  import torch
  from detectron2.utils.visualizer import Visualizer
  import slowfast.utils.logging
  from slowfast.utils.misc import get_class_names
label_desc: Use image node_id 7 for calling the VideoVisualizer obj's underlying member method code with example usage: obj._get_thres_array(common_class_names) without return types