class_docstr: Base AudioVideo Reader
  
  Args:
      opts: command line arguments
      is_training: Training or validation mode. Default: `False`.
class_docstr_tok: ["Base","AudioVideo","Reader","Args",":","opts",":","command","line","arguments","is_training",":","Training","or","validation","mode",".","Default",":","`","False","`","."]
code: def dummy_audio_video_clips(
      self,
      clips_per_video: int,
      num_frames_to_sample: int,
      height: int,
      width: int,
  ) -> Dict:
      # [K, C, T, H, W] or # [K, N, T, H, W]
      # K --> number of clips, C --> Image channels, N --> Number of frames per clip,
      # H --> Height, W --> Width.
      video_tensor_size = (
          (clips_per_video, 3, num_frames_to_sample, height, width)
          if self.channel_first_format
          else (clips_per_video, num_frames_to_sample, 3, height, width)
      )
  
      video_clips = torch.zeros(
          size=video_tensor_size,
          dtype=torch.float32,
          device=torch.device("cpu"),
      )
      audio_fps = 16000
      video_fps = 30
      # Dummy clips for audio
      audio_tensor_size = (
          clips_per_video,
          int(num_frames_to_sample * audio_fps / video_fps),
          1,
      )
      audio_clips = torch.zeros(
          size=audio_tensor_size,
          dtype=torch.float32,
          device=torch.device("cpu"),
      )
  
      metadata = {
          "video_fps": video_fps,
          "audio_fps": audio_fps,
          "filename": "dummy.mp4",
      }
      return {
          "video": video_clips,
          "audio": audio_clips,
          "metadata": metadata,
      }
  
imports: import argparse
  import random
  from typing import Dict, Optional, Tuple
  import av
  import numpy
  import torch
  import torch.nn.functional
  from torch import Tensor
  from data.transforms import BaseTransformation
  from data.transforms import image_pil
  from data.transforms.common import Compose
  from options.utils import extend_selected_args_with_prefix, extract_opts_with_prefix_replacement
  from utils import logger
label_desc: Use image node_id 13 for calling the BaseAVReader obj's underlying member method code with example usage: obj.dummy_audio_video_clips(clips_per_video, num_frames_to_sample, height, width) and returns: dict