class_docstr: Linear multiple rewards generator.
code: def __call__(self, x, enable_noise=False):
      # `x` is of shape [`batch_size`, 'context_dim']
      # `theta` is of shape [`context_dim`, 'num_rewards']
      # The result `predicted_rewards` has shape [`batch_size`, `num_rewards`]
      predicted_rewards = np.matmul(x, self.thetas)
      return predicted_rewards
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import gin
  import numpy
  from six.moves import range
  import tensorflow
  from tf_agents.bandits.agents import utils
  from tf_agents.bandits.environments import wheel_py_environment
label_desc: Use image node_id 2 for calling the LinearNormalMultipleRewards obj's underlying member method code with example usage: obj.__call__(x, enable_noise) and returns: predicted_rewards