class_docstr: Some transformer encoder layers
class_docstr_tok: ["Some","transformer","encoder","layers"]
code: def __init__(
      self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"
  ):
      super().__init__()
      self.device = device
      self.transformer = TransformerWrapper(
          num_tokens=vocab_size,
          max_seq_len=max_seq_len,
          attn_layers=Encoder(dim=n_embed, depth=n_layer),
      )
  
imports: import torch
  import torch.nn
  from functools import partial
  import clip
  from einops import rearrange, repeat
  from transformers import CLIPTokenizer, CLIPTextModel
  import kornia
  from ldm.modules.x_transformer import Encoder, TransformerWrapper
label_desc: Use image node_id 1 to create a new TransformerEmbedder object from inherited base classes: AbstractEncoder with example: obj = TransformerEmbedder(n_embed, n_layer, vocab_size, max_seq_len, device)