code: def forward(self, x):
      b = x.shape[0]
  
      if (
          not exists(self.positional_emb)
          and x.size(1) < self.sequence_length
      ):
          x = F.pad(
              x,
              (0, 0, 0, self.n_channels - x.size(1)),
              mode="constant",
              value=0,
          )
  
      if not self.seq_pool:
          cls_token = repeat(self.class_emb, "1 1 d -> b 1 d", b=b)
          x = torch.cat((cls_token, x), dim=1)
  
      if exists(self.positional_emb):
          x += self.positional_emb
  
      x = self.dropout(x)
  
      for blk in self.blocks:
          x = blk(x)
  
      x = self.norm(x)
  
      if self.seq_pool:
          attn_weights = rearrange(
              self.attention_pool(x), "b n 1 -> b n"
          )
          x = einsum(
              "b n, b n d -> b d", attn_weights.softmax(dim=1), x
          )
      else:
          x = x[:, 0]
  
      return self.fc(x)
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
label_desc: Use image node_id 3 for calling the TransformerClassifier obj's underlying member method code with example usage: obj.forward(x) and returns: self