code: def __init__(
      self,
      time_step_spec=None,
      action_spec=None,
      training_data_spec=None,
      train_sequence_length=None,
  ):
      if time_step_spec is None:
          obs_spec = {"obs": tf.TensorSpec([], tf.float32)}
          time_step_spec = ts.time_step_spec(obs_spec)
      action_spec = action_spec or ()
      policy = random_tf_policy.RandomTFPolicy(
          time_step_spec, action_spec
      )
      super(MyAgent, self).__init__(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          policy=policy,
          collect_policy=policy,
          train_sequence_length=train_sequence_length,
          training_data_spec=training_data_spec,
      )
      self._as_trajectory = data_converter.AsTrajectory(
          self.data_context, sequence_length=train_sequence_length
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import numpy
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import test_util
  from tf_agents.agents import tf_agent
  from tf_agents.policies import random_tf_policy
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 to create a new MyAgent object from inherited base classes: tf_agent with example: obj = MyAgent(time_step_spec, action_spec, training_data_spec, train_sequence_length)