code: def __init__(
      self,
      seq_pool=True,
      embedding_dim=768,
      num_layers=12,
      num_heads=12,
      mlp_ratio=4.0,
      num_classes=1000,
      dropout_rate=0.1,
      attention_dropout=0.1,
      stochastic_depth_rate=0.1,
      positional_embedding="sine",
      sequence_length=None,
      *args,
      **kwargs,
  ):
      super().__init__()
      assert positional_embedding in {"sine", "learnable", "none"}
  
      dim_feedforward = int(embedding_dim * mlp_ratio)
      self.embedding_dim = embedding_dim
      self.sequence_length = sequence_length
      self.seq_pool = seq_pool
  
      assert (
          exists(sequence_length) or positional_embedding == "none"
      ), (
          f"Positional embedding is set to {positional_embedding} and"
          f" the sequence length was not specified."
      )
  
      if not seq_pool:
          sequence_length += 1
          self.class_emb = nn.Parameter(
              torch.zeros(1, 1, self.embedding_dim)
          )
      else:
          self.attention_pool = nn.Linear(self.embedding_dim, 1)
  
      if positional_embedding == "none":
          self.positional_emb = None
      elif positional_embedding == "learnable":
          self.positional_emb = nn.Parameter(
              torch.zeros(1, sequence_length, embedding_dim)
          )
          nn.init.trunc_normal_(self.positional_emb, std=0.2)
      else:
          self.register_buffer(
              "positional_emb",
              sinusoidal_embedding(sequence_length, embedding_dim),
          )
  
      self.dropout = nn.Dropout(p=dropout_rate)
  
      dpr = [
          x.item()
          for x in torch.linspace(0, stochastic_depth_rate, num_layers)
      ]
  
      self.blocks = nn.ModuleList(
          [
              TransformerEncoderLayer(
                  d_model=embedding_dim,
                  nhead=num_heads,
                  dim_feedforward=dim_feedforward,
                  dropout=dropout_rate,
                  attention_dropout=attention_dropout,
                  drop_path_rate=layer_dpr,
              )
              for layer_dpr in dpr
          ]
      )
  
      self.norm = nn.LayerNorm(embedding_dim)
  
      self.fc = nn.Linear(embedding_dim, num_classes)
      self.apply(self.init_weight)
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
label_desc: Use image node_id 1 to create a new TransformerClassifier object from inherited base classes: nn with example: obj = TransformerClassifier(seq_pool, embedding_dim, num_layers, num_heads, mlp_ratio, num_classes, dropout_rate, attention_dropout, stochastic_depth_rate, positional_embedding, sequence_length)