docstr: Provide different tensors as test cases.
  
  Yield:
      Tuple[torch.Tensor]: tensors as test case input.
docstr_tok: ["Provide","different","tensors","as","test","cases",".","Yield",":","Tuple","[","torch.Tensor","]",":","tensors","as","test","case","input","."]
code: def _get_inputs(
      channel: int = 3,
      clip_length: int = 64,
      audio_clip_length: int = 128,
      crop_size: int = 224,
      audio_size: int = 80,
      frame_ratios: Tuple[int] = (8, 2),
      audio_frame_ratio: int = 1,
  ) -> Tuple[torch.Tensor]:
      """
      Provide different tensors as test cases.
  
      Yield:
          Tuple[torch.Tensor]: tensors as test case input.
      """
      # Prepare random inputs as test cases.
      shape = (1, channel, clip_length, crop_size, crop_size)
      audio_shape = (1, 1, audio_clip_length, 1, audio_size)
      output = uniform_temporal_subsample_repeated(
          torch.rand(shape), frame_ratios=frame_ratios, temporal_dim=2
      )
      yield output + uniform_temporal_subsample_repeated(
          torch.rand(audio_shape),
          frame_ratios=(audio_frame_ratio,),
          temporal_dim=2,
      )
  
file_path: pytorchvideo/tests/test_models_audio_visual_slowfast.py
imports: import itertools
  import unittest
  from typing import Tuple
  import torch
  from pytorchvideo.models.audio_visual_slowfast import create_audio_visual_slowfast
  from pytorchvideo.transforms.functional import uniform_temporal_subsample_repeated
  from torch import nn
label_desc: Use image node_id 3 for calling the TestAVSlowFast obj's underlying member method code with example usage: obj._get_inputs(channel, clip_length, audio_clip_length, crop_size, audio_size, frame_ratios, audio_frame_ratio) without return types