content: import torch.nn as nn
  import torch.nn.functional as F
  
  
  class SdfMlp(nn.Module):
      def __init__(self, input_dim, hidden_dim=512, bias=True):
          super().__init__()
          self.input_dim = input_dim
          self.hidden_dim = hidden_dim
  
          self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias)
          self.fc2 = nn.Linear(hidden_dim, hidden_dim, bias=bias)
          self.fc3 = nn.Linear(hidden_dim, 4, bias=bias)
  
  
      def forward(self, input):
          x = F.relu(self.fc1(input))
          x = F.relu(self.fc2(x))
          out = self.fc3(x)
          return out
  
  
  class RgbMlp(nn.Module):
      def __init__(self, input_dim, hidden_dim=512, bias=True):
          super().__init__()
          self.input_dim = input_dim
          self.hidden_dim = hidden_dim
  
          self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias)
          self.fc2 = nn.Linear(hidden_dim, hidden_dim, bias=bias)
          self.fc3 = nn.Linear(hidden_dim, 3, bias=bias)
  
      def forward(self, input):
          x = F.relu(self.fc1(input))
          x = F.relu(self.fc2(x))
          out = self.fc3(x)
  
          return out
  
      