code: def scale_by_tanh(x, low, high):
      scale = (high - low) / 2
      scale = torch.unsqueeze(torch.from_numpy(scale), dim=0).to(
          x.device
      )
      mean = (high + low) / 2
      mean = torch.unsqueeze(torch.from_numpy(mean), dim=0).to(x.device)
      return torch.tanh(x) * scale + mean
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from pfrl.action_value import DiscreteActionValue, DistributionalDiscreteActionValue, QuadraticActionValue
  from pfrl.functions.lower_triangular_matrix import lower_triangular_matrix
  from pfrl.initializers import init_chainer_default
  from pfrl.nn import Lambda
  from pfrl.nn.mlp import MLP
  from pfrl.q_function import StateQFunction
label_desc: Use image node_id 1 for calling a global function with example usage: scale_by_tanh(x, low, high) and returns: unknown