code: def encode_text(self, text):
      pad_index = self.tokenizer.vocab["[PAD]"]
  
      attn_mask = text.not_equal(paddle.to_tensor(pad_index)).cast(
          self.dtype
      )
  
      x = self.bert(text, attention_mask=attn_mask)[0].cast(
          self.dtype
      )  # [batch_size, seq_length, hidden_size]
      return x[:, 0, :] @ self.text_projection
  
imports: from collections import OrderedDict
  from typing import Tuple
  from typing import Union
  import numpy
  import paddle
  import paddle.nn.functional
  from disco_diffusion_cnclip_vitb16.cn_clip.clip import _tokenizer
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.configuration_bert import BertConfig
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.modeling_bert import BertModel
  from paddle import nn
  from paddle.nn import MultiHeadAttention
label_desc: Use image node_id 4 for calling the CLIP obj's underlying member method code with example usage: obj.encode_text(text) and returns: unknown