code: def testGammaMean(self):
      alpha_v = np.array([1.0, 3.0, 2.5])
      beta_v = np.array([1.0, 4.0, 5.0])
      gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v)
      self.assertEqual(gamma.mean().get_shape(), (3,))
      if not stats:
          return
      expected_means = stats.gamma.mean(alpha_v, scale=1 / beta_v)
      self.assertAllClose(self.evaluate(gamma.mean()), expected_means)
  
imports: import importlib
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops.distributions import gamma
  from tensorflow.python.ops.distributions import kullback_leibler
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 7 for calling the GammaTest obj's underlying member method code with example usage: obj.testGammaMean() without return types