code: def testEquivAdagradwithoutRegularization(self):
      # The v1 optimizers do not support eager execution
      with ops.Graph().as_default():
          for dtype in [dtypes.half, dtypes.float32]:
              with self.cached_session():
                  val0, val1 = self.applyOptimizer(
                      ftrl.FtrlOptimizer(
                          3.0,
                          # Adagrad learning rate
                          learning_rate_power=-0.5,
                          initial_accumulator_value=0.1,
                          l1_regularization_strength=0.0,
                          l2_regularization_strength=0.0,
                      ),
                      dtype,
                  )
  
              with self.cached_session():
                  val2, val3 = self.applyOptimizer(
                      adagrad.AdagradOptimizer(
                          3.0, initial_accumulator_value=0.1
                      ),
                      dtype,
                  )
  
              self.assertAllCloseAccordingToType(
                  val0, val2, half_rtol=2e-3
              )
              self.assertAllCloseAccordingToType(
                  val1, val3, half_rtol=2e-3
              )
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.training import adagrad
  from tensorflow.python.training import ftrl
  from tensorflow.python.training import gradient_descent
label_desc: Use image node_id 14 for calling the FtrlOptimizerTest obj's underlying member method code with example usage: obj.testEquivAdagradwithoutRegularization() without return types