docstr: Provide different tensors as test cases.
  
  Yield:
      (torch.tensor): tensor as test case input.
docstr_tok: ["Provide","different","tensors","as","test","cases",".","Yield",":","(","torch.tensor",")",":","tensor","as","test","case","input","."]
code: def _get_inputs(
      channel: int = 3, clip_length: int = 4, crop_size: int = 112
  ) -> 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)
  
file_path: pytorchvideo/tests/test_models_csn.py
imports: import itertools
  import os
  import unittest
  import numpy
  import torch
  from pytorchvideo.models.csn import create_csn
  from pytorchvideo.models.resnet import create_bottleneck_block
  from torch import nn
label_desc: Use image node_id 3 for calling the TestCSN obj's underlying member method code with example usage: obj._get_inputs(channel, clip_length, crop_size) without return types