code: def __init__(self, z_dim, map_hidden_dim, map_output_dim):
      super().__init__()
  
      self.network = nn.Sequential(
          nn.Linear(z_dim, map_hidden_dim),
          nn.LeakyReLU(0.2, inplace=True),
          nn.Linear(map_hidden_dim, map_hidden_dim),
          nn.LeakyReLU(0.2, inplace=True),
          nn.Linear(map_hidden_dim, map_hidden_dim),
          nn.LeakyReLU(0.2, inplace=True),
          nn.Linear(map_hidden_dim, map_output_dim),
      )
  
      self.network.apply(kaiming_leaky_init)
      with torch.no_grad():
          self.network[-1].weight *= 0.25
  
imports: import os
  import time
  import timm
  import glob
  import numpy
  import os.path
  import torch
  import torch.distributed
  from torch import nn
  import torch.nn.functional
  from .iresnet import get_model
label_desc: Use image node_id 1 to create a new CustomMappingNetwork object from inherited base classes: nn with example: obj = CustomMappingNetwork(z_dim, map_hidden_dim, map_output_dim)