code: def forward(self, batch: tensor_dict_type, **kwargs: Any) -> Tensor:
      batch = shallow_copy_dict(batch)
      net = batch[INPUT_KEY]
      net = self.from_latent(net)
      batch[INPUT_KEY] = net
      return self.decoder(batch, **kwargs)
  
imports: import torch.nn
  from torch import Tensor
  from typing import Any
  from typing import List
  from typing import Union
  from typing import Optional
  from cftool.misc import shallow_copy_dict
  from cftool.types import tensor_dict_type
  from .schema import DecoderMixin
  from .schema import Decoder1DMixin
  from ....constants import INPUT_KEY
  from ....modules.blocks import get_conv_blocks
  from ....modules.blocks import Lambda
  from ....modules.blocks import Linear
  from ....modules.blocks import Activation
  from ....modules.blocks import ResidualBlock
  from ....modules.blocks import ChannelPadding
  from ....modules.blocks import UpsampleConv2d
label_desc: Use image node_id 2 for calling the VanillaDecoder1D obj's underlying member method code with example usage: obj.forward(batch) and returns: self