class_docstr: A register to regist backbones for the flexible unet. All backbones can be found in
  register_dict. Please notice each output of backbone must be 2x downsample in spatial
  dimension of last output. For example, if given a 512x256 2D image and a backbone with
  4 outputs. Then spatial size of each encoder output should be 256x128, 128x64, 64x32
  and 32x16.
class_docstr_tok: ["A","register","to","regist","backbones","for","the","flexible","unet",".","All","backbones","can","be","found","in","register_dict",".","Please","notice","each","output","of","backbone","must","be","2x","downsample","in","spatial","dimension","of","last","output",".","For","example",",","if","given","a","512x256","2D","image","and","a","backbone","with","4","outputs",".","Then","spatial","size","of","each","encoder","output","should","be","256x128",",","128x64",",","64x32","and","32x16","."]
code: def __init__(self):
      self.register_dict = {}
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Sequence
  from pydoc import locate
  from typing import Any
  import torch
  from torch import nn
  from monai.networks.blocks import BaseEncoder, UpSample
  from monai.networks.layers.factories import Conv
  from monai.networks.layers.utils import get_act_layer
  from monai.networks.nets import EfficientNetEncoder
  from monai.networks.nets.basic_unet import UpCat
  from monai.utils import InterpolateMode, optional_import
label_desc: Use image node_id 1 to create a new FlexUNetEncoderRegister object  with example: obj = FlexUNetEncoderRegister()