code: def testSharing(self):
      for dtype in self.float_types | self.complex_types:
          # TODO: test fails for float16 due to excessive precision requirements.
          if dtype in [np.float16, dtypes.bfloat16.as_numpy_dtype]:
              continue
          with self.session(), self.test_scope():
              variable_scope.get_variable_scope().set_use_resource(True)
  
              # Initialize variables for numpy implementation.
              m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
              var0_np = np.array([1.0, 2.0], dtype=dtype)
              grads0_np = np.array([0.1, 0.1], dtype=dtype)
              var1_np = np.array([3.0, 4.0], dtype=dtype)
              grads1_np = np.array([0.01, 0.01], dtype=dtype)
  
              var0 = resource_variable_ops.ResourceVariable(var0_np)
              var1 = resource_variable_ops.ResourceVariable(var1_np)
              grads0 = array_ops.placeholder(dtype)
              grads1 = array_ops.placeholder(dtype)
              opt = adam.AdamOptimizer()
              update1 = opt.apply_gradients(
                  zip([grads0, grads1], [var0, var1])
              )
              update2 = opt.apply_gradients(
                  zip([grads0, grads1], [var0, var1])
              )
              self.evaluate(variables.global_variables_initializer())
  
              beta1_power, beta2_power = opt._get_beta_accumulators()
  
              # Fetch params to validate initial values
              self.assertAllClose([1.0, 2.0], self.evaluate(var0))
              self.assertAllClose([3.0, 4.0], self.evaluate(var1))
  
              # Run 3 steps of intertwined Adam1 and Adam2.
              for t in range(1, 4):
                  self.assertAllCloseAccordingToType(
                      0.9**t, self.evaluate(beta1_power)
                  )
                  self.assertAllCloseAccordingToType(
                      0.999**t, self.evaluate(beta2_power)
                  )
                  if t % 2 == 0:
                      update1.run(
                          feed_dict={
                              grads0: grads0_np,
                              grads1: grads1_np,
                          }
                      )
                  else:
                      update2.run(
                          feed_dict={
                              grads0: grads0_np,
                              grads1: grads1_np,
                          }
                      )
  
                  var0_np, m0, v0 = adam_update_numpy(
                      var0_np, grads0_np, t, m0, v0
                  )
                  var1_np, m1, v1 = adam_update_numpy(
                      var1_np, grads1_np, t, m1, v1
                  )
  
                  # Validate updated params
                  self.assertAllCloseAccordingToType(
                      var0_np, self.evaluate(var0)
                  )
                  self.assertAllCloseAccordingToType(
                      var1_np, self.evaluate(var1)
                  )
  
file_path: tensorflow/tensorflow/compiler/tests/adam_test.py
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.training import adam
label_desc: Use image node_id 3 for calling the AdamOptimizerTest obj's underlying member method code with example usage: obj.testSharing() without return types