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 that a wrapped function with signature of the form
  func(arr1, arr2, ...) acting on non-trainable input returns non-trainable output
docstr_tok: ["Test","that","a","wrapped","function","with","signature","of","the","form","func","(","arr1",",","arr2",",","...",")","acting","on","non-trainable","input","returns","non-trainable","output"]
code: def test_wrapped_function_nontrainable_variable_args(self):
      """Test that a wrapped function with signature of the form
      func(arr1, arr2, ...) acting on non-trainable input returns non-trainable output
      """
      arr1 = np.array([0, 1], requires_grad=False)
      arr2 = np.array([2, 3], requires_grad=False)
  
      res = np.arctan2(arr1, arr2)
      assert not res.requires_grad
  
      # If one of the inputs is trainable, the output always is.
      arr1.requires_grad = True
      res = np.arctan2(arr1, arr2)
      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 7 for calling the TestNumpyIntegration obj's underlying member method code with example usage: obj.test_wrapped_function_nontrainable_variable_args() without return types