code: def forward(self, x):
      bs = x.shape[0]
  
      maxlen_eye = Tensor.eye(x.shape[1])
      maxlen_eye = maxlen_eye.unsqueeze(0).expand(
          [bs, *maxlen_eye.shape]
      )
  
      onehot_feat = x.one_hot(self.syms)
  
      onehot = maxlen_eye.cat(onehot_feat, dim=2).flatten(end_dim=1)
  
      x = onehot.dot(self.embed).reshape((bs, x.shape[1], -1))
      x = x.sequential(self.tbs)
      x = x.reshape((-1, x.shape[-1])).dot(self.final).log_softmax()
      return x.reshape((bs, -1, x.shape[-1]))
  
imports: from tinygrad import Tensor
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x) and returns: x