class_docstr: A simple multiple-rewards environment.
class_docstr_tok: ["A","simple","multiple-rewards","environment","."]
code: def __init__(self, observation_shape, batch_size=1, num_rewards=1):
      self._num_rewards = num_rewards
      reward_spec = tensor_spec.TensorSpec(
          shape=[self._num_rewards], dtype=tf.float32, name="reward"
      )
  
      observation_spec = tensor_spec.TensorSpec(
          shape=observation_shape, dtype=tf.float32, name="observation"
      )
      time_step_spec = ts.time_step_spec(observation_spec, reward_spec)
      super(MultipleRewardsEnvironment, self).__init__(
          time_step_spec=time_step_spec, batch_size=batch_size
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.environments import bandit_tf_environment
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import common
label_desc: Use image node_id 1 to create a new MultipleRewardsEnvironment object from inherited base classes: bandit_tf_environment with example: obj = MultipleRewardsEnvironment(observation_shape, batch_size, num_rewards)