docstr: Provide different tensors as test cases.
  
  Yield:
      (torch.tensor): tensor as test case input.
code: def _get_inputs(
      channel: int = 3, clip_length: int = 8, crop_size: int = 224
  ) -> torch.tensor:
      """
      Provide different tensors as test cases.
  
      Yield:
          (torch.tensor): tensor as test case input.
      """
      # Prepare random inputs as test cases.
      shapes = (
          (1, channel, clip_length, crop_size, crop_size),
          (2, channel, clip_length, crop_size, crop_size),
      )
      for shape in shapes:
          yield torch.rand(shape)
  
imports: import itertools
  import os
  import unittest
  import numpy
  import torch
  from pytorchvideo.models.head import ResNetBasicHead
  from pytorchvideo.models.net import Net
  from pytorchvideo.models.resnet import BottleneckBlock, create_acoustic_bottleneck_block, create_acoustic_resnet, create_bottleneck_block, create_res_block, create_res_stage, create_resnet, create_resnet_with_roi_head, ResBlock, ResStage, SeparableBottleneckBlock
  from pytorchvideo.models.stem import ResNetBasicStem
  from torch import nn
label_desc: Use image node_id 9 for calling the TestResNet obj's underlying member method code with example usage: obj._get_inputs(channel, clip_length, crop_size) without return types