class_docstr: Visualize action predictions for videos or folder of images with precomputed
  and ground-truth boxes in AVA format.
class_docstr_tok: ["Visualize","action","predictions","for","videos","or","folder","of","images","with","precomputed","and","ground-truth","boxes","in","AVA","format","."]
docstr: Return a video writer object.
  Args:
      path (str): path to the output video file.
docstr_tok: ["Return","a","video","writer","object",".","Args",":","path","(","str",")",":","path","to","the","output","video","file","."]
code: def get_output_file(self, path):
      """
      Return a video writer object.
      Args:
          path (str): path to the output video file.
      """
      return cv2.VideoWriter(
          filename=path,
          fourcc=cv2.VideoWriter_fourcc(*"mp4v"),
          fps=float(30),
          frameSize=(self.display_width, self.display_height),
          isColor=True,
      )
  
imports: import numpy
  import os
  import cv2
  import torch
  import tqdm
  import slowfast.utils.checkpoint
  import slowfast.utils.logging
  from slowfast.datasets.ava_helper import parse_bboxes_file
  from slowfast.datasets.cv2_transform import scale, scale_boxes
  from slowfast.datasets.utils import get_sequence
  from slowfast.models import build_model
  from slowfast.utils import misc
  from slowfast.utils.env import pathmgr
  from slowfast.visualization.utils import process_cv2_inputs
  from slowfast.visualization.video_visualizer import VideoVisualizer
label_desc: Use image node_id 2 for calling the AVAVisualizerWithPrecomputedBox obj's underlying member method code with example usage: obj.get_output_file(path) and returns: cv2