code: def test_small_input(self):
      device = torch.device("cuda")
      for kernel_size in [3, 5]:
          padding = kernel_size // 2
          N, C, H, W = shape = (1, 1, kernel_size - 1, kernel_size - 1)
  
          inputs = torch.rand(shape).to(
              device
          )  # input size is smaller than kernel size
  
          offset_channels = kernel_size * kernel_size * 2
          offset = torch.randn(
              (N, offset_channels, H, W), dtype=torch.float32
          ).to(device)
          deform = DeformConv(
              C, C, kernel_size=kernel_size, padding=padding
          ).to(device)
          output = deform(inputs, offset)
          self.assertTrue(output.shape == inputs.shape)
  
          mask_channels = kernel_size * kernel_size
          mask = torch.ones(
              (N, mask_channels, H, W), dtype=torch.float32
          ).to(device)
          modulate_deform = ModulatedDeformConv(
              C, C, kernel_size, padding=padding, bias=False
          ).to(device)
          output = modulate_deform(inputs, offset, mask)
          self.assertTrue(output.shape == inputs.shape)
  
file_path: detectron2/tests/layers/test_deformable.py
imports: import numpy
  import unittest
  import torch
  from detectron2.layers import DeformConv, ModulatedDeformConv
  from detectron2.utils.env import TORCH_VERSION
label_desc: Use image node_id 4 for calling the DeformableTest obj's underlying member method code with example usage: obj.test_small_input() without return types