code: def testBNWithZeroBatchInput(self, distribution, fused):
      distribution.extended.experimental_enable_get_next_as_optional = (
          True
      )
      with distribution.scope():
          inputs = (
              np.random.random((0, 4, 4, 3)).astype(np.float32) + 100
          )
          targets = np.random.random((0, 4, 4, 3)).astype(np.float32)
          bn = normalization.BatchNormalization(
              axis=3, epsilon=1e-3, momentum=0.9, fused=fused
          )
          optimizer = gradient_descent.GradientDescentOptimizer(0.01)
  
          @def_function.function
          def train_step():
              def step_fn(inputs, targets):
                  with backprop.GradientTape() as tape:
                      outputs = bn.apply(inputs, training=True)
                      loss = losses.mean_squared_error(targets, outputs)
                  grads = tape.gradient(loss, bn.variables)
                  optimizer.apply_gradients(zip(grads, bn.variables))
                  return loss
  
              return distribution.run(step_fn, args=(inputs, targets))
  
          for _ in range(100):
              np_output = train_step().numpy()
              self.assertEqual(0.0, np_output)
  
          # Verify that the statistics and weights are not changed after training.
          self.assertAllEqual([0, 0, 0], bn.moving_mean.numpy())
          self.assertAllEqual([1, 1, 1], bn.moving_variance.numpy())
          self.assertAllEqual([1, 1, 1], bn.gamma.numpy())
          self.assertAllEqual([0, 0, 0], bn.beta.numpy())
  
          @def_function.function
          def test_step():
              def step_fn(inputs):
                  outputs = bn.apply(inputs, training=False)
                  return outputs
  
              return distribution.run(step_fn, args=(inputs,))
  
          # Test inference.
          self.assertAllEqual(
              np.zeros(shape=(0, 4, 4, 3), dtype=np.float32),
              test_step().numpy(),
          )
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import strategy_combinations
  from tensorflow.python.distribute import test_util
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.layers import normalization
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.losses import losses
  from tensorflow.python.platform import test
  from tensorflow.python.training import gradient_descent
label_desc: Use image node_id 2 for calling the NormalizationTest obj's underlying member method code with example usage: obj.testBNWithZeroBatchInput(distribution, fused) and returns: distribution, distribution, loss, outputs