class_docstr: Uses the OpenCLIP transformer encoder for text
class_docstr_tok: ["Uses","the","OpenCLIP","transformer","encoder","for","text"]
code: def encode_with_transformer(self, text):
      x = self.model.token_embedding(
          text
      )  # [batch_size, n_ctx, d_model]
      x = x + self.model.positional_embedding
      x = x.permute(1, 0, 2)  # NLD -> LND
      x = self.text_transformer_forward(
          x, attn_mask=self.model.attn_mask
      )
      x = x.permute(1, 0, 2)  # LND -> NLD
      x = self.model.ln_final(x)
      return x
  
imports: import torch
  import torch.nn
  import kornia
  from torch.utils.checkpoint import checkpoint
  from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
  import open_clip
  from ldm.util import default, count_params, autocast
  from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
  from ldm.modules.diffusionmodules.openaimodel import Timestep
label_desc: Use image node_id 4 for calling the FrozenOpenCLIPEmbedder obj's underlying member method code with example usage: obj.encode_with_transformer(text) and returns: x