class_docstr: Test the frobenius_inner_product method.
class_docstr_tok: ["Test","the","frobenius_inner_product","method","."]
docstr: Test that the calculated gradient is correct.
docstr_tok: ["Test","that","the","calculated","gradient","is","correct","."]
code: def test_frobenius_inner_product_gradient(self):
      """Test that the calculated gradient is correct."""
      A = onp.array([[1.0, 2.3], [-1.3, 2.4]])
      B = torch.autograd.Variable(
          torch.randn(2, 2).type(torch.float), requires_grad=True
      )
      result = fn.frobenius_inner_product(A, B)
      result.backward()
      grad = B.grad
  
      assert np.allclose(grad, A)
  
imports: import numpy
  import pytest
  from pennylane import numpy, math
label_desc: Use image node_id 2 for calling the TestFrobeniusInnerProduct obj's underlying member method code with example usage: obj.test_frobenius_inner_product_gradient() without return types