code: def equivAdagradTest_FtrlPart(self, steps, dtype):
      var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
      opt = ftrl.FtrlOptimizer(
          3.0,
          learning_rate_power=-0.5,  # using Adagrad learning rate
          initial_accumulator_value=0.1,
          l1_regularization_strength=0.0,
          l2_regularization_strength=0.0,
      )
      ftrl_update = opt.apply_gradients(
          zip([grads0, grads1], [var0, var1])
      )
      self.evaluate(variables.global_variables_initializer())
      # Fetch params to validate initial values
      self.assertAllClose([0.0, 0.0], self.evaluate(var0))
      self.assertAllClose([0.0, 0.0], self.evaluate(var1))
  
      # Run Ftrl for a few steps
      for _ in range(steps):
          ftrl_update.run()
  
      return self.evaluate(var0), self.evaluate(var1)
  
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  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 2 for calling the FtrlOptimizerTest obj's underlying member method code with example usage: obj.equivAdagradTest_FtrlPart(steps, dtype) and returns: self,  self