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 binary operators on one trainable and one non-trainable
  arrays result in trainable output.
docstr_tok: ["Test","that","binary","operators","on","one","trainable","and","one","non-trainable","arrays","result","in","trainable","output","."]
code: def test_binary_operator_mixed_trainable_left(self):
      """Test that binary operators on one trainable and one non-trainable
      arrays result in trainable output."""
      x = np.array([[1, 2], [3, 4]], requires_grad=True)
      y = np.array([[5, 6], [7, 8]], requires_grad=False)
  
      res = x + y
      assert isinstance(res, np.tensor)
      assert res.requires_grad
  
      res = x - y
      assert isinstance(res, np.tensor)
      assert res.requires_grad
  
      res = x / y
      assert isinstance(res, np.tensor)
      assert res.requires_grad
  
      res = x * y
      assert isinstance(res, np.tensor)
      assert res.requires_grad
  
      res = x @ y
      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 16 for calling the TestNumpyIntegration obj's underlying member method code with example usage: obj.test_binary_operator_mixed_trainable_left() without return types