code: import torch.nn as nn
  import torch.nn.functional as F
  from utils.noisy_liner import NoisyLinear
  from torch.nn import LayerNorm
  
  class NoisyRNNAgent(nn.Module):
      def __init__(self, input_shape, args):
          super(NoisyRNNAgent, self).__init__()
          self.args = args
  
          self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim)
          self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim)
          self.fc2 = NoisyLinear(args.rnn_hidden_dim, args.n_actions, True, args.device)
  
          if getattr(args, "use_layer_norm", False):
              self.layer_norm = LayerNorm(args.rnn_hidden_dim)
  
      def init_hidden(self):
          # make hidden states on same device as model
          return self.fc1.weight.new(1, self.args.rnn_hidden_dim).zero_()
  
      def forward(self, inputs, hidden_state):
          b, a, e = inputs.size()
          
          inputs = inputs.view(-1, e)
          x = F.relu(self.fc1(inputs), inplace=True)
          h_in = hidden_state.reshape(-1, self.args.rnn_hidden_dim)
          hh = self.rnn(x, h_in)
  
          if getattr(self.args, "use_layer_norm", False):
              q = self.fc2(self.layer_norm(hh))
          else:
              q = self.fc2(hh)
  
          return q.view(b, a, -1), hh.view(b, a, -1)
apis: torch.nn.GRUCell torch.nn.Linear torch.nn.LayerNorm