code: def scale_by_tanh(x, low, high):
      xp = cuda.get_array_module(x.array)
      scale = (high - low) / 2
      scale = xp.expand_dims(
          xp.asarray(scale, dtype=np.float32), axis=0
      )
      mean = (high + low) / 2
      mean = xp.expand_dims(xp.asarray(mean, dtype=np.float32), axis=0)
      return F.tanh(x) * scale + mean
  
imports: import chainer
  from chainer import cuda
  from chainer import functions
  from chainer import links
  import numpy
  import chainerrl
  from chainerrl.action_value import DiscreteActionValue
  from chainerrl.action_value import DistributionalDiscreteActionValue
  from chainerrl.action_value import QuadraticActionValue
  from chainerrl.functions.lower_triangular_matrix import lower_triangular_matrix
  from chainerrl.links.mlp import MLP
  from chainerrl.links.mlp_bn import MLPBN
  from chainerrl.q_function import StateQFunction
  from chainerrl.recurrent import RecurrentChainMixin
label_desc: Use image node_id 1 for calling a global function with example usage: scale_by_tanh(x, low, high) and returns: unknown