class_docstr: Test that the wrapped NumPy functionality integrates well
  with standard NumPy functions.
class_docstr_tok: ["Test","that","the","wrapped","NumPy","functionality","integrates","well","with","standard","NumPy","functions","."]
docstr: Test behaviour of a wrapped function on a vanilla NumPy
  array.
docstr_tok: ["Test","behaviour","of","a","wrapped","function","on","a","vanilla","NumPy","array","."]
code: def test_wrapped_function_on_array(self):
      """Test behaviour of a wrapped function on a vanilla NumPy
      array."""
      res = np.sin(onp.array([0, 1, 2]))
      expected = onp.sin(onp.array([0, 1, 2]))
      assert np.all(res == expected)
  
      # the result has been converted into a tensor
      assert isinstance(res, np.tensor)
      assert res.requires_grad
  
imports: import numpy
  import pytest
  from autograd.numpy.numpy_boxes import ArrayBox
  import pennylane
  from pennylane import numpy
  from pennylane.numpy.tensor import tensor_to_arraybox
label_desc: Use image node_id 8 for calling the TestNumpyIntegration obj's underlying member method code with example usage: obj.test_wrapped_function_on_array() without return types