class_docstr: Tests for _compute_gradients method.
class_docstr_tok: ["Tests","for","_compute_gradients","method","."]
code: def testBaselineWithCallableLossNoNoise(
      self, optimizer_class, num_microbatches
  ):
      var0 = tf.Variable([1.0, 2.0])
      var1 = tf.Variable([3.0])
      data0 = tf.Variable(
          [[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]]
      )
      data1 = tf.Variable([[8.0], [2.0], [3.0], [1.0]])
      expected_grad0 = [-2.5, -2.5]
      expected_grad1 = [-0.5]
  
      optimizer = optimizer_class(
          l2_norm_clip=100.0,
          noise_multiplier=0.0,
          num_microbatches=num_microbatches,
          learning_rate=2.0,
      )
  
      loss = lambda: self._loss(data0, var0) + self._loss(data1, var1)
  
      grads_and_vars = optimizer._compute_gradients(loss, [var0, var1])
  
      self.assertAllCloseAccordingToType(
          expected_grad0, grads_and_vars[0][0]
      )
      self.assertAllCloseAccordingToType(
          expected_grad1, grads_and_vars[1][0]
      )
  
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 2 for calling the DPOptimizerComputeGradientsTest obj's underlying member method code with example usage: obj.testBaselineWithCallableLossNoNoise(optimizer_class, num_microbatches) without return types