code: def _prepare_alphas(action_spec, dtype=tf.float32):
      num_actions = action_spec.maximum - action_spec.minimum + 1
      alphas = [
          tf.compat.v2.Variable(
              tf.ones([], dtype=dtype), name="alpha_{}".format(k)
          )
          for k in range(num_actions)
      ]
      return alphas
  
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.policies import bernoulli_thompson_sampling_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 1 for calling a global function with example usage: _prepare_alphas(action_spec, dtype) and returns: alphas