code: def initialize(self):
      super().initialize()
  
      # Initial variables summaries
      if self.summaries == "all" or "variables" in self.summaries:
          with self.summarizer.as_default():
              for variable in self.trainable_variables:
                  name = variable.name
                  assert name[-2] == ":"
                  if name.startswith(self.name + "/"):
                      # Add prefix self.name since otherwise different scope from later summaries
                      name = (
                          self.name
                          + "/variables/"
                          + name[len(self.name) + 1 : -2]
                      )
                  else:
                      name = name[:-2]
                  x = tf.math.reduce_mean(input_tensor=variable)
                  tf.summary.scalar(
                      name=name, data=x, step=self.updates
                  )
  
imports: import logging
  import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import ModuleDict, memory_modules, optimizer_modules, parameter_modules, SignatureDict, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util, VariableDict
  from tensorforce.core.models import Model
  from tensorforce.core.networks import Preprocessor
  from tensorforce.core.objectives import objective_modules
  from tensorforce.core.policies import policy_modules, StochasticPolicy
label_desc: Use image node_id 3 for calling the TensorforceModel obj's underlying member method code with example usage: obj.initialize() without return types