code: def weight_init(shape, mode, fan_in, fan_out):
      if mode == "xavier_uniform":
          return np.sqrt(6 / (fan_in + fan_out)) * (
              torch.rand(*shape) * 2 - 1
          )
      if mode == "xavier_normal":
          return np.sqrt(2 / (fan_in + fan_out)) * torch.randn(*shape)
      if mode == "kaiming_uniform":
          return np.sqrt(3 / fan_in) * (torch.rand(*shape) * 2 - 1)
      if mode == "kaiming_normal":
          return np.sqrt(1 / fan_in) * torch.randn(*shape)
      raise ValueError(f'Invalid init mode "{mode}"')
  
imports: import torch
  import torch.nn
  import torchvision
  import numpy
  import torch
  from torch.nn.functional import silu
  from einops import rearrange, repeat
  from utils.general_utils import matrix_to_quaternion, quaternion_raw_multiply
  from utils.graphics_utils import fov2focal
label_desc: Use image node_id 1 for calling a global function with example usage: weight_init(shape, mode, fan_in, fan_out) and returns: unknown, unknown, unknown, unknown