code: def test_forward_params(
      self, p, p_batch, same_on_batch, num, seed, device, dtype
  ):
      input_shape = (12,)
      torch.manual_seed(seed)
      augmentation = _BasicAugmentationBase(p, p_batch, same_on_batch)
      with patch.object(
          augmentation, "generate_parameters", autospec=True
      ) as generate_parameters:
          generate_parameters.side_effect = lambda shape: {
              "degrees": torch.arange(
                  0, shape[0], device=device, dtype=dtype
              )
          }
          output = augmentation.forward_parameters(input_shape)
          assert "batch_prob" in output
          assert (
              len(output["degrees"])
              == output["batch_prob"].sum().item()
              == num
          )
  
imports: from unittest.mock import patch
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia.testing
  from kornia.augmentation._2d.base import AugmentationBase2D
  from kornia.augmentation._2d.geometric.affine import RandomAffine
  from kornia.augmentation._2d.intensity.gaussian_blur import RandomGaussianBlur
  from kornia.augmentation._3d.geometric.affine import RandomAffine3D
  from kornia.augmentation._3d.intensity.motion_blur import RandomMotionBlur3D
  from kornia.augmentation.base import _BasicAugmentationBase
  from kornia.testing import assert_close
label_desc: Use image node_id 3 for calling the TestBasicAugmentationBase obj's underlying member method code with example usage: obj.test_forward_params(p, p_batch, same_on_batch, num, seed, device, dtype) without return types