class_docstr: Mixed class token concat operation.
  
  Supported arguments are:
  
  - ``embed_dim``
  
  Prefix of cls_token will be sliced.
code: def forward_with_args(
      self, embed_dim, inputs: torch.Tensor
  ) -> torch.Tensor:
      cls_token = self.freeze_weight(embed_dim)["cls_token"]
      assert isinstance(cls_token, torch.Tensor)
  
      return torch.cat(
          (cls_token.expand(inputs.shape[0], -1, -1), inputs), dim=1
      )
  
imports: from __future__ import annotations
  from typing import Tuple, cast, Any, Dict
  import torch
  import torch.nn
  import torch.nn.functional
  import nni
  from nni.mutable import Categorical, MutableExpression, frozen_context, ensure_frozen, Mutable
  from nni.nas.nn.pytorch import ParametrizedModule, ModelSpace, MutableModule, Repeat, MutableConv2d, MutableLinear, MutableLayerNorm
  from nni.nas.space import current_model
  from nni.nas.oneshot.pytorch.supermodule.operation import MixedOperation
  from nni.nas.profiler.pytorch.flops import FlopsResult
  from nni.nas.profiler.pytorch.utils import MutableShape, ShapeTensor, profiler_leaf_module
  from .utils.pretrained import load_pretrained_weight
  from .utils.nn import DropPath
label_desc: Use image node_id 3 for calling the MixedClassToken obj's underlying member method code with example usage: obj.forward_with_args(embed_dim, inputs) and returns: torch