code: def test_shape_large(self, device, dtype):
      F_mat = torch.rand(1, 2, 3, 3, device=device, dtype=dtype)
      K1 = torch.rand(1, 2, 3, 3, device=device, dtype=dtype)
      K2 = torch.rand(
          1, 1, 3, 3, device=device, dtype=dtype
      )  # check broadcasting
      E_mat = epi.essential_from_fundamental(F_mat, K1, K2)
      assert E_mat.shape == (1, 2, 3, 3)
  
imports: import pytest
  import test_common
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.geometry.epipolar
  from kornia.testing import assert_close
label_desc: Use image node_id 4 for calling the TestEssentialFromFundamental obj's underlying member method code with example usage: obj.test_shape_large(device, dtype) without return types