text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from networks.network_utils import hidden_init
  
  class MADDPGActorVersion1(nn.Module):
      def __init__(self, state_size, action_size, seed, fc1_units, fc2_units):
          """Initialize parameters and build model.
          Params
          ======
              state_size (int): Dimension of each state
              action_size (int): Dimension of each action
              seed (int): Random seed
              fc1_units (int): Number of nodes in first hidden layer
              fc2_units (int): Number of nodes in second hidden layer
          """
          super().__init__()
          
          self.seed = torch.manual_seed(seed)
  
          self.fc1 = nn.Linear(state_size, fc1_units)
          self.fc2 = nn.Linear(fc1_units, fc2_units)
          self.fc3 = nn.Linear(fc2_units, action_size)
          self.reset_parameters()
          
      def reset_parameters(self):
  
          self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
          self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
          self.fc3.weight.data.uniform_(-3e-3, 3e-3)
         
      
      def forward(self, state):
          """Build an actor (policy) network that maps states -> actions."""
          x = F.relu(self.fc1(state))
          x = F.relu(self.fc2(x))
          
          return torch.tanh(self.fc3(x))
  