class_docstr: Tests for the batch_transform decorator differentiability
class_docstr_tok: ["Tests","for","the","batch_transform","decorator","differentiability"]
docstr: Test that a batch transform is differentiable when using
  autograd
docstr_tok: ["Test","that","a","batch","transform","is","differentiable","when","using","autograd"]
code: def test_differentiable_autograd(self, diff_method):
      """Test that a batch transform is differentiable when using
      autograd"""
      dev = qml.device("default.qubit", wires=2)
      qnode = qml.QNode(self.circuit, dev, diff_method=diff_method)
  
      def cost(x, weights):
          return self.my_transform(qnode, weights)(x)
  
      weights = np.array([0.1, 0.2], requires_grad=True)
      x = np.array(0.543, requires_grad=True)
  
      res = cost(x, weights)
      assert np.allclose(res, self.expval(x, weights))
  
      grad = qml.grad(cost)(x, weights)
      expected = qml.grad(self.expval)(x, weights)
      assert all(np.allclose(g, e) for g, e in zip(grad, expected))
  
imports: import functools
  import pytest
  import pennylane
  from pennylane import numpy
label_desc: Use image node_id 4 for calling the TestBatchTransformGradients obj's underlying member method code with example usage: obj.test_differentiable_autograd(diff_method) and returns: self