class_docstr: Generates a text file with your predicted tracked objects, in the MOTChallenge format.
  It needs the 'input_path', which is the path to the sequence being processed,
  the 'save_path', and optionally the 'information_file' (in case you don't give an
  'information_file', is assumed there is one in the input_path folder).
class_docstr_tok: ["Generates","a","text","file","with","your","predicted","tracked","objects",",","in","the","MOTChallenge","format",".","It","needs","the","'input_path","'",",","which","is","the","path","to","the","sequence","being","processed",",","the","'save_path","'",",","and","optionally","the","'information_file","'","(","in","case","you","do","n't","give","an","'information_file","'",",","is","assumed","there","is","one","in","the","input_path","folder",")","."]
code: def __init__(self, input_path, save_path=".", information_file=None):
      file_name = os.path.split(input_path)[1]
  
      if information_file is None:
          seqinfo_path = os.path.join(input_path, "seqinfo.ini")
          information_file = InformationFile(file_path=seqinfo_path)
  
      self.length = information_file.search(variable_name="seqLength")
  
      predictions_folder = os.path.join(save_path, "predictions")
      if not os.path.exists(predictions_folder):
          os.makedirs(predictions_folder)
  
      out_file_name = os.path.join(
          predictions_folder, file_name + ".txt"
      )
      self.text_file = open(out_file_name, "w+")
  
      self.frame_number = 1
  
imports: import os
  import numpy
  from rich import print
  from rich.progress import track
  from norfair import Detection
  from collections import OrderedDict
label_desc: Use image node_id 1 to create a new PredictionsTextFile object  with example: obj = PredictionsTextFile(input_path, save_path, information_file)