class_docstr: Tests for is_independent, which tests a function to be
  independent of its inputs, using Tensorflow.
class_docstr_tok: ["Tests","for","is_independent",",","which","tests","a","function","to","be","independent","of","its","inputs",",","using","Tensorflow","."]
docstr: Tests that kwargs are taken into account when checking
  independence of outputs.
docstr_tok: ["Tests","that","kwargs","are","taken","into","account","when","checking","independence","of","outputs","."]
code: def test_kwargs_are_considered(self):
      """Tests that kwargs are taken into account when checking
      independence of outputs."""
      f = lambda x, kw=False: 0.1 * x if kw else tf.constant(0.2)
  
      def _jac(x, kw):
          with tf.GradientTape() as tape:
              out = f(x, kw)
          return tape.jacobian(out, x)
  
      args = (tf.Variable(0.2),)
      assert is_independent(f, self.interface, args)
      assert not is_independent(f, self.interface, args, {"kw": True})
      assert is_independent(_jac, self.interface, args, {"kw": True})
  
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 5 for calling the TestIsIndependentTensorflow obj's underlying member method code with example usage: obj.test_kwargs_are_considered() and returns: tape