class_docstr: Tests for is_independent, which tests a function to be
  independent of its inputs, using Autograd.
class_docstr_tok: ["Tests","for","is_independent",",","which","tests","a","function","to","be","independent","of","its","inputs",",","using","Autograd","."]
docstr: Tests that no deprecation arises when using qml.jacobian with
  qml.math.is_independent.
docstr_tok: ["Tests","that","no","deprecation","arises","when","using","qml.jacobian","with","qml.math.is_independent","."]
code: def test_no_trainable_params_deprecation_jac(self, recwarn):
      """Tests that no deprecation arises when using qml.jacobian with
      qml.math.is_independent."""
  
      def lin(x, weights=None):
          return np.dot(x, weights)
  
      x = pnp.array([0.2, 9.1, -3.2], requires_grad=True)
      weights = pnp.array([1.1, -0.7, 1.8], requires_grad=True)
      jac = qml.jacobian(lin)
      assert qml.math.is_independent(
          jac, "autograd", (x,), {"weights": weights}
      )
      assert len(recwarn) == 0
  
imports: import pytest
  import numpy
  from pennylane import numpy
  import pennylane
  from pennylane.math import is_independent
  from pennylane.math.is_independent import _get_random_args
label_desc: Use image node_id 7 for calling the TestIsIndependentAutograd obj's underlying member method code with example usage: obj.test_no_trainable_params_deprecation_jac(recwarn) and returns: np