docstr: Provide different tensors as test cases.
  
  Yield:
      (torch.tensor): tensor as test case input.
code: def _get_inputs(dim_in: int = 3) -> torch.tensor:
      """
      Provide different tensors as test cases.
  
      Yield:
          (torch.tensor): tensor as test case input.
      """
      # Prepare random segmentation as test cases.
      shapes = (
          # Forward succeeded.
          (1, dim_in, 3, 7, 7),
          (1, dim_in, 5, 7, 7),
          (1, dim_in, 7, 7, 7),
          (2, dim_in, 3, 7, 7),
          (4, dim_in, 3, 7, 7),
          (8, dim_in, 3, 7, 7),
          (2, dim_in, 3, 7, 14),
          (2, dim_in, 3, 14, 7),
          (2, dim_in, 3, 14, 14),
          # Forward failed.
          (8, dim_in * 2, 3, 7, 7),
          (8, dim_in * 4, 5, 7, 7),
      )
      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 8 for calling the TestBottleneckBlock obj's underlying member method code with example usage: obj._get_inputs(dim_in) without return types