code: def independent_act(
      self,
      *,
      states,
      internals=None,
      auxiliaries=None,
      deterministic=None,
  ):
      if internals is None:
          assert len(self.internals_spec) == 0
          internals = TensorDict()
      if auxiliaries is None:
          assert len(self.auxiliaries_spec) == 0
          auxiliaries = TensorDict()
      assert deterministic is not None
      batch_size = tf_util.cast(
          x=tf.shape(input=states.value())[0], dtype="int"
      )
  
      # Input assertions
      assertions = list()
      if self.config.create_tf_assertions:
          true = tf_util.constant(value=True, dtype="bool")
          assertions.extend(
              self.states_spec.tf_assert(
                  x=states,
                  batch_size=batch_size,
                  message="Agent.independent_act: invalid {issue} for {name} state input.",
              )
          )
          assertions.extend(
              self.internals_spec.tf_assert(
                  x=internals,
                  batch_size=batch_size,
                  message="Agent.independent_act: invalid {issue} for {name} internal input.",
              )
          )
          assertions.extend(
              self.auxiliaries_spec.tf_assert(
                  x=auxiliaries,
                  batch_size=batch_size,
                  message="Agent.independent_act: invalid {issue} for {name} input.",
              )
          )
          assertions.extend(
              self.deterministic_spec.tf_assert(
                  x=deterministic,
                  message="Agent.independent_act: invalid {issue} for deterministic input.",
              )
          )
          # Mask assertions
          if self.config.enable_int_action_masking:
              for name, spec in self.actions_spec.items():
                  if spec.type == "int":
                      assertions.append(
                          tf.debugging.assert_equal(
                              x=tf.reduce_all(
                                  input_tensor=tf.math.reduce_any(
                                      input_tensor=auxiliaries[name][
                                          "mask"
                                      ],
                                      axis=(spec.rank + 1),
                                  )
                              ),
                              y=true,
                              message="Agent.independent_act: at least one action has to be valid.",
                          )
                      )
  
      with tf.control_dependencies(control_inputs=assertions):
          # Core act
          parallel = tf_util.zeros(
              shape=tf.expand_dims(batch_size, axis=0), dtype="int"
          )
          actions, internals = self.core_act(
              states=states,
              internals=internals,
              auxiliaries=auxiliaries,
              parallel=parallel,
              deterministic=deterministic,
              independent=True,
          )
          # Skip action assertions
  
          if len(self.internals_spec) > 0:
              return actions, internals
          else:
              return actions
  
imports: from collections import OrderedDict
  import logging
  import os
  import time
  import h5py
  import numpy
  import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import ArrayDict, Module, SignatureDict, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util, VariableDict
  from tensorforce.core.layers import Layer
label_desc: Use image node_id 16 for calling the Model obj's underlying member method code with example usage: obj.independent_act() and returns: actions,  internals, actions