code: def __init__(
      self,
      feature_vector_size,
      d_in,
      d_out,
      dims,
      geometric_init=True,
      bias=1.0,
      skip_in=(),
      weight_norm=True,
      multires=0,
  ):
      super().__init__()
  
      dims = [d_in] + dims + [d_out + feature_vector_size]
  
      self.embed_fn = None
      if multires > 0:
          embed_fn, input_ch = get_embedder(multires)
          self.embed_fn = embed_fn
          dims[0] = input_ch
  
      self.num_layers = len(dims)
      self.skip_in = skip_in
  
      for l in range(0, self.num_layers - 1):
          if l + 1 in self.skip_in:
              out_dim = dims[l + 1] - dims[0]
          else:
              out_dim = dims[l + 1]
  
          lin = nn.Linear(dims[l], out_dim)
  
          if geometric_init:
              if l == self.num_layers - 2:
                  torch.nn.init.normal_(
                      lin.weight,
                      mean=np.sqrt(np.pi) / np.sqrt(dims[l]),
                      std=0.0001,
                  )
                  torch.nn.init.constant_(lin.bias, -bias)
              elif multires > 0 and l == 0:
                  torch.nn.init.constant_(lin.bias, 0.0)
                  torch.nn.init.constant_(lin.weight[:, 3:], 0.0)
                  torch.nn.init.normal_(
                      lin.weight[:, :3],
                      0.0,
                      np.sqrt(2) / np.sqrt(out_dim),
                  )
              elif multires > 0 and l in self.skip_in:
                  torch.nn.init.constant_(lin.bias, 0.0)
                  torch.nn.init.normal_(
                      lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim)
                  )
                  torch.nn.init.constant_(
                      lin.weight[:, -(dims[0] - 3) :], 0.0
                  )
              else:
                  torch.nn.init.constant_(lin.bias, 0.0)
                  torch.nn.init.normal_(
                      lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim)
                  )
  
          if weight_norm:
              lin = nn.utils.weight_norm(lin)
  
          setattr(self, "lin" + str(l), lin)
  
      self.softplus = nn.Softplus(beta=100)
  
imports: import torch
  import torch.nn
  import numpy
  import trimesh
  import os
  from utils import rend_util
  from model.embedder import *
  from model.ray_tracing import RayTracing
  from model.sample_network import SampleNetwork
label_desc: Use image node_id 1 to create a new ImplicitNetwork object from inherited base classes: nn with example: obj = ImplicitNetwork(feature_vector_size, d_in, d_out, dims, geometric_init, bias, skip_in, weight_norm, multires)