code: def _static_unroll_single_step(
      cell, inputs, reset_mask, state, zero_state, training
  ):
      """Helper for dynamic_unroll which runs a single step."""
  
      def _squeeze(t):
          if not isinstance(t, tf.TensorArray) and t.shape.rank > 0:
              return tf.squeeze(t, [0])
          else:
              return t
  
      # Remove time dimension.
      inputs = tf.nest.map_structure(_squeeze, inputs)
      if reset_mask is not None:
          reset_mask = _squeeze(reset_mask)
          state = tf.nest.map_structure(
              lambda s, s_zero: _maybe_reset_state(
                  reset_mask, s_zero, s
              ),
              state,
              zero_state,
          )
  
      outputs, final_state = cell(inputs, state, training=training)
      outputs = tf.nest.map_structure(
          lambda t: tf.expand_dims(t, 1), outputs
      )
  
      return (outputs, final_state)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.utils import common
label_desc: Use image node_id 5 for calling a global function with example usage: _static_unroll_single_step(cell, inputs, reset_mask, state, zero_state, training) and returns: outputs,  final_state, tf, t