class_docstr: The ``FaceDetector`` is a :class:`~flash.Task` for detecting faces in images.
  
  For more details, see :ref:`face_detection`.
  
  Args:
      model: a string of :attr`_models`. Defaults to 'lffd_slim'.
      pretrained: Whether the model from fastface should be loaded with it's pretrained weights.
      loss_fn: the function(s) to update the model with. Has no effect for fastface models.
      metrics: The provided metrics. All metrics here will be logged to progress bar and the respective logger.
          Changing this argument currently has no effect.
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      learning_rate: The learning rate to use for training.
      kwargs: additional kwargs nessesary for initializing face detector backbone
docstr: Return the module attributes of the model to be frozen.
docstr_tok: ["Return","the","module","attributes","of","the","model","to","be","frozen","."]
code: def modules_to_freeze(
      self,
  ) -> Union[Module, Iterable[Union[Module, Iterable]]]:
      """Return the module attributes of the model to be frozen."""
      return self.model.backbone
  
imports: from typing import Any, Iterable, List, Optional, Union
  from torch import Tensor
  from torch.nn import Module
  from flash.core.data.io.input import DataKeys
  from flash.core.model import Task
  from flash.core.utilities.imports import _FASTFACE_AVAILABLE
  from flash.core.utilities.stability import beta
  from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
  from flash.image.face_detection.backbones import FACE_DETECTION_BACKBONES
  from flash.image.face_detection.output_transform import FaceDetectionOutputTransform
label_desc: Use image node_id 13 for calling the FaceDetector obj's underlying member method code with example usage: obj.modules_to_freeze() and returns: self