text: 
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  def normalized_columns_initializer(weights, std=1.0):
      out = torch.randn(weights.size())
      out *= std / torch.sqrt(out.pow(2).sum(1, keepdim=True))
      return out
  
  
  def weights_init(m):
      classname = m.__class__.__name__
      if classname.find('Conv') != -1:
          weight_shape = list(m.weight.data.size())
          fan_in = np.prod(weight_shape[1:4])
          fan_out = np.prod(weight_shape[2:4]) * weight_shape[0]
          w_bound = np.sqrt(6. / (fan_in + fan_out))
          m.weight.data.uniform_(-w_bound, w_bound)
          m.bias.data.fill_(0)
      elif classname.find('Linear') != -1:
          weight_shape = list(m.weight.data.size())
          fan_in, fan_out = weight_shape[1], weight_shape[0]
          w_bound = np.sqrt(6. / (fan_in + fan_out))
          m.weight.data.uniform_(-w_bound, w_bound)
          m.bias.data.fill_(0)
  
  
  class ActorCritic(torch.nn.Module):
      def __init__(self, num_inputs, num_actions):
          super(ActorCritic, self).__init__()
          self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1)
          self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
          self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
          self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
  
          self.lstm = nn.LSTMCell(32 * 3 * 3, 256)
  
          self.critic_linear = nn.Linear(256, 1)
          self.actor_linear = nn.Linear(256, num_actions)
  
          self.apply(weights_init)
          self.actor_linear.weight.data = normalized_columns_initializer(
              self.actor_linear.weight.data, 0.01)
          self.actor_linear.bias.data.fill_(0)
          self.critic_linear.weight.data = normalized_columns_initializer(
              self.critic_linear.weight.data, 1.0)
          self.critic_linear.bias.data.fill_(0)
  
          self.lstm.bias_ih.data.fill_(0)
          self.lstm.bias_hh.data.fill_(0)
  
          self.train()
  
      def forward(self, inputs):
          inputs, (hx, cx) = inputs
          x = F.elu(self.conv1(inputs))
          x = F.elu(self.conv2(x))
          x = F.elu(self.conv3(x))
          x = F.elu(self.conv4(x))
  
          x = x.view(-1, 32 * 3 * 3)
          hx, cx = self.lstm(x, (hx, cx))
          x = hx
  
          return self.critic_linear(x), self.actor_linear(x), (hx, cx)
  
  