code: def __init__(
      self,
      in_channels: int,
      num_classes: int,
      img_size: Optional[int] = None,
      latent_dim: int = 128,
      aux_num_classes: Optional[Dict[str, int]] = None,
      *,
      encoder1d: str = "vanilla",
      encoder1d_config: Optional[Dict[str, Any]] = None,
      encoder1d_pretrained_name: Optional[str] = None,
      encoder1d_pretrained_path: Optional[str] = None,
      encoder1d_pretrained_strict: bool = True,
  ):
      super().__init__()
      self.img_size = img_size
      # encoder1d
      if encoder1d_config is None:
          encoder1d_config = {}
      encoder1d_config.setdefault("img_size", img_size)
      encoder1d_config.setdefault("in_channels", in_channels)
      encoder1d_config.setdefault("latent_dim", latent_dim)
      if aux_num_classes is not None:
          encoder1d_config.setdefault(
              "aux_heads", sorted(aux_num_classes)
          )
      self.encoder1d = make_encoder(
          encoder1d, encoder1d_config, is_1d=True
      )
      assert isinstance(self.encoder1d, torch.nn.Module)
      if encoder1d_pretrained_path is None:
          if encoder1d_pretrained_name is not None:
              encoder1d_pretrained_path = download_model(
                  encoder1d_pretrained_name
              )
      if encoder1d_pretrained_path is not None:
          print_info(
              f"loading pretrained encoder1d from '{encoder1d_pretrained_path}'"
          )
          d = torch.load(encoder1d_pretrained_path)
          self.encoder1d.load_state_dict(
              d, strict=encoder1d_pretrained_strict
          )
      # head
      main_head = Linear(latent_dim, num_classes)
      self.head = None
      self.aux_keys = None
      if aux_num_classes is None:
          self.head = main_head
      else:
          heads = {LATENT_KEY: main_head}
          self.aux_keys = []
          for key, n in aux_num_classes.items():
              heads[key] = Linear(latent_dim, n)
              self.aux_keys.append(key)
          self.heads = torch.nn.ModuleDict(heads)
  
imports: import torch
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Optional
  from cftool.misc import print_info
  from cftool.misc import shallow_copy_dict
  from cftool.array import softmax
  from cftool.types import tensor_dict_type
  from ..encoder import run_encoder
  from ..encoder import make_encoder
  from ....schema import IDLModel
  from ....schema import TrainerState
  from ....constants import INPUT_KEY
  from ....constants import LATENT_KEY
  from ....constants import PREDICTIONS_KEY
  from ....misc.toolkit import download_model
  from ....modules.blocks import Linear
label_desc: Use image node_id 1 to create a new VanillaClassifier object from inherited base classes: IDLModel with example: obj = VanillaClassifier(in_channels, num_classes, img_size, latent_dim, aux_num_classes)