code: def forward(self, batch: tensor_dict_type, **kwargs: Any) -> Tensor:
      net = self._inject_cond(batch)[INPUT_KEY]
      determinate = kwargs.pop("determinate", False)
      for block in self.decoder:
          kw = {}
          if isinstance(block, UpsampleConv2d):
              kw["determinate"] = determinate
          net = block(net, **kw)
      net = self.resize(net, determinate=determinate)
      if self.final_activation is not None:
          net = self.final_activation(net)
      return net
  
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 VanillaDecoder obj's underlying member method code with example usage: obj.forward(batch) and returns: net