code: def forward(self, net: Tensor, **kwargs: Any) -> Tensor:
      determinate = kwargs.pop("determinate", False)
      patches, hw = self.to_patches(net, determinate=determinate)
      kwargs["hw"] = hw
      kwargs["hwp"] = *net.shape[-2:], self.to_patches.patch_size
      if check_requires(
          self.encoder.forward, "determinate", strict=False
      ):
          kwargs["determinate"] = determinate
      return self.encoder(patches, **kwargs)
  
imports: import torch
  import torch.nn
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Type
  from typing import Optional
  from cftool.misc import safe_execute
  from cftool.misc import check_requires
  from cftool.misc import WithRegister
  from cftool.types import tensor_dict_type
  from ....schema import _forward
  from ....schema import TrainerState
  from ....constants import INPUT_KEY
  from ....constants import LATENT_KEY
  from ....misc.toolkit import eval_context
  from ....misc.toolkit import get_device
  from ....modules.blocks import ImgToPatches
label_desc: Use image node_id 3 for calling the EncoderFromPatchesMixin obj's underlying member method code with example usage: obj.forward(net) and returns: self