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(input_dim: int = 8) -> torch.tensor:
      """
      Provide different tensors as test cases.
  
      Yield:
          (torch.tensor): tensor as test case input.
      """
      # Prepare random tensor as test cases.
      shapes = (
          # Forward succeeded.
          (1, input_dim, 5, 7, 7),
          (2, input_dim, 5, 7, 7),
          (4, input_dim, 5, 7, 7),
          (4, input_dim, 5, 7, 7),
          (4, input_dim, 7, 7, 7),
          (4, input_dim, 7, 7, 14),
          (4, input_dim, 7, 14, 7),
          (4, input_dim, 7, 14, 14),
          # Forward failed.
          (8, input_dim * 2, 3, 7, 7),
          (8, input_dim * 4, 5, 7, 7),
      )
      for shape in shapes:
          yield torch.rand(shape)
  
file_path: pytorchvideo/tests/test_layers_nonlocal_net.py
imports: import itertools
  import unittest
  from typing import Iterable
  import numpy
  import torch
  from pytorchvideo.layers.nonlocal_net import create_nonlocal, NonLocal
  from torch import nn
label_desc: Use image node_id 4 for calling the TestNonlocal obj's underlying member method code with example usage: obj._get_inputs(input_dim) without return types