code: def __init__(
      self,
      model: nn.Module,
      size_offsets: List[int],
      mode: str = "bilinear",
      align_corners: bool = False,
      augment_fn: Callable = ms_image_augment,
      deaugment_fn: Union[
          Callable, Dict[str, Callable]
      ] = ms_image_deaugment,
  ):
      if isinstance(deaugment_fn, Mapping):
          self.keys = set(deaugment_fn.keys())
      else:
          self.keys = None
  
      super().__init__()
      self.model = model
      self.size_offsets = size_offsets
      self.mode = mode
      self.align_corners = align_corners
      self.augment_fn = augment_fn
      self.deaugment_fn = deaugment_fn
  
imports: from collections import defaultdict
  from functools import partial
  from typing import Tuple, List, Optional, Union, Callable, Dict, Mapping
  import torch
  from torch import Tensor, nn
  from .None import functional
  from ..utils.support import pytorch_toolbelt_deprecated
label_desc: Use image node_id 1 to create a new MultiscaleTTA object from inherited base classes: nn with example: obj = MultiscaleTTA(model, size_offsets, mode, align_corners, augment_fn, deaugment_fn)