code: def reset_parameters(self) -> None:
      if self.token_embedding is not None:
          nn.init.normal_(self.token_embedding.weight, std=0.02)
      tld = self.text_latent_dim
      if self.text_transformer is not None:
          text_encoder = self.text_transformer.encoder
          nn.init.normal_(
              text_encoder.pos_encoding.pos_encoding, std=0.01
          )
          proj_std = (tld**-0.5) * (
              (2 * self.text_num_layers) ** -0.5
          )
          attn_std = tld**-0.5
          fc_std = (2 * tld) ** -0.5
          for block in text_encoder.mixing_blocks:
              attn = block.token_mixing.net
              mlp = block.channel_mixing.net
              nn.init.normal_(attn.in_w, std=attn_std)
              nn.init.normal_(attn.out_linear.weight, std=proj_std)
              nn.init.normal_(mlp[0].weight, std=fc_std)
              nn.init.normal_(mlp[3].weight, std=proj_std)
      if self.text_projection is not None:
          nn.init.normal_(self.text_projection.weight, std=tld**-0.5)
          nn.init.zeros_(self.text_projection.bias)
  
imports: import torch
  from torch import nn
  from torch import Tensor
  from typing import Optional
  from cftool.cv import to_rgb
  from cftool.array import l2_normalize
  from torchvision.transforms import Resize
  from torchvision.transforms import Compose
  from torchvision.transforms import ToTensor
  from torchvision.transforms import Normalize
  from torchvision.transforms import CenterCrop
  from torchvision.transforms import InterpolationMode
  from .schema import IPerceptor
  from ...constants import INPUT_KEY
  from ...constants import LATENT_KEY
  from ...modules.blocks import HijackLinear
  from ..cv.encoder.transformer import ViTEncoder
  from ..nlp.encoder.transformer import TeTEncoder
label_desc: Use image node_id 4 for calling the CLIP obj's underlying member method code with example usage: obj.reset_parameters() without return types