class_docstr: Tests for get_gradient method.
  
  Since get_gradients must run in graph mode, the method is tested within
  the Estimator framework.
class_docstr_tok: ["Tests","for","get_gradient","method",".","Since","get_gradients","must","run","in","graph","mode",",","the","method","is","tested","within","the","Estimator","framework","."]
docstr: Tests that DP optimizers work with tf.estimator.
code: def testNoiseMultiplier(
      self,
      optimizer_class,
      l2_norm_clip,
      noise_multiplier,
      num_microbatches,
      requires_varlist,
  ):
      """Tests that DP optimizers work with tf.estimator."""
      tf.random.set_seed(2)
      linear_regressor = tf_estimator.Estimator(
          model_fn=self._make_linear_model_fn(
              optimizer_class,
              l2_norm_clip,
              noise_multiplier,
              num_microbatches,
              requires_varlist=requires_varlist,
              learning_rate=1.0,
          )
      )
  
      true_weights = np.zeros((1000, 1), dtype=np.float32)
      true_bias = np.array([0.0]).astype(np.float32)
  
      train_data = np.zeros((16, 1000), dtype=np.float32)
      train_labels = np.matmul(train_data, true_weights) + true_bias
  
      def train_input_fn():
          return tf.data.Dataset.from_tensor_slices(
              (train_data, train_labels)
          ).batch(16)
  
      linear_regressor.train(input_fn=train_input_fn, steps=1)
  
      kernel_value = linear_regressor.get_variable_value("dense/kernel")
      self.assertNear(
          np.std(kernel_value),
          l2_norm_clip * noise_multiplier / num_microbatches,
          0.5,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow_privacy.privacy.optimizers import dp_optimizer_keras
  from tensorflow_privacy.privacy.optimizers import dp_optimizer_keras_vectorized
label_desc: Use image node_id 4 for calling the DPOptimizerGetGradientsTest obj's underlying member method code with example usage: obj.testNoiseMultiplier(optimizer_class, l2_norm_clip, noise_multiplier, num_microbatches, requires_varlist) and returns: tf