code: def encode_text(self, text):
      x = self.token_embedding(text).type(
          self.dtype
      )  # [batch_size, n_ctx, d_model]
  
      x = x + self.positional_embedding.type(self.dtype)
      x = x.permute(1, 0, 2)  # NLD -> LND
      x = self.transformer(x)
      x = x.permute(1, 0, 2)  # LND -> NLD
      x = self.ln_final(x).type(self.dtype)
  
      # x.shape = [batch_size, n_ctx, transformer.width]
      # take features from the eot embedding (eot_token is the highest number in each sequence)
      x = (
          x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
          @ self.text_projection
      )
  
      return x
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 6 for calling the CLIP obj's underlying member method code with example usage: obj.encode_text(text) and returns: x