code: def experience(
      self, *, states, internals, auxiliaries, actions, terminal, reward
  ):
      true = tf_util.constant(value=True, dtype="bool")
      one = tf_util.constant(value=1, dtype="int")
      batch_size = tf_util.cast(
          x=tf.shape(input=terminal)[0], dtype="int"
      )
  
      # Input assertions
      assertions = list()
      if self.config.create_tf_assertions:
          zero = tf_util.constant(value=0, dtype="int")
          assertions.extend(
              self.states_spec.tf_assert(
                  x=states,
                  batch_size=batch_size,
                  message="Agent.experience: invalid {issue} for {name} state input.",
              )
          )
          assertions.extend(
              self.internals_spec.tf_assert(
                  x=internals,
                  batch_size=batch_size,
                  message="Agent.experience: invalid {issue} for {name} internal input.",
              )
          )
          assertions.extend(
              self.auxiliaries_spec.tf_assert(
                  x=auxiliaries,
                  batch_size=batch_size,
                  message="Agent.experience: invalid {issue} for {name} input.",
              )
          )
          assertions.extend(
              self.actions_spec.tf_assert(
                  x=actions,
                  batch_size=batch_size,
                  message="Agent.experience: invalid {issue} for {name} action input.",
              )
          )
          assertions.extend(
              self.terminal_spec.tf_assert(
                  x=terminal,
                  batch_size=batch_size,
                  message="Agent.experience: invalid {issue} for terminal input.",
              )
          )
          assertions.extend(
              self.reward_spec.tf_assert(
                  x=reward,
                  batch_size=batch_size,
                  message="Agent.experience: invalid {issue} for reward input.",
              )
          )
          # Mask assertions
          if self.config.enable_int_action_masking:
              for name, spec in self.actions_spec.items():
                  if spec.type == "int" and spec.num_values is not None:
                      is_valid = tf.reduce_all(
                          input_tensor=tf.gather(
                              params=auxiliaries[name]["mask"],
                              indices=tf.expand_dims(
                                  input=actions[name],
                                  axis=(spec.rank + 1),
                              ),
                              batch_dims=(spec.rank + 1),
                          )
                      )
                      assertions.append(
                          tf.debugging.assert_equal(
                              x=is_valid,
                              y=true,
                              message="Agent.experience: invalid action / mask.",
                          )
                      )
          # Assertion: buffer indices is zero
          assertions.append(
              tf.debugging.assert_equal(
                  x=tf.math.reduce_sum(
                      input_tensor=self.buffer_index, axis=0
                  ),
                  y=zero,
                  message="Agent.experience: cannot be called mid-episode.",
              )
          )
          # Assertion: one terminal
          num_terms = tf.math.count_nonzero(
              input=terminal, dtype=tf_util.get_dtype(type="int")
          )
          assertions.append(
              tf.debugging.assert_equal(
                  x=num_terms,
                  y=one,
                  message="Agent.experience: input contains none or more than one terminal.",
              )
          )
          # Assertion: terminal is last timestep in batch
          assertions.append(
              tf.debugging.assert_greater_equal(
                  x=terminal[-1],
                  y=one,
                  message="Agent.experience: terminal is not the last input timestep.",
              )
          )
  
      with tf.control_dependencies(control_inputs=assertions):
          # Preprocessing
          for name in states:
              if name in self.state_preprocessing:
                  states[name] = self.state_preprocessing[name].apply(
                      x=states[name],
                      deterministic=true,
                      independent=False,
                  )
          if self.reward_processing is not None:
              reward = self.reward_processing.apply(
                  x=reward, deterministic=true, independent=False
              )
  
          # Core experience
          experienced = self.core_experience(
              states=states,
              internals=internals,
              auxiliaries=auxiliaries,
              actions=actions,
              terminal=terminal,
              reward=reward,
          )
  
      # Increment timestep and episode
      with tf.control_dependencies(control_inputs=(experienced,)):
          assignments = list()
          assignments.append(
              self.timesteps.assign_add(
                  delta=batch_size, read_value=False
              )
          )
          assignments.append(
              self.episodes.assign_add(delta=one, read_value=False)
          )
  
      with tf.control_dependencies(control_inputs=assignments):
          timestep = tf_util.identity(input=self.timesteps)
          episode = tf_util.identity(input=self.episodes)
          return timestep, episode
  
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 10 for calling the TensorforceModel obj's underlying member method code with example usage: obj.experience() and returns: timestep,  episode