class_docstr: Wrap the original efficientnet to an encoder for flexible-unet.
class_docstr_tok: ["Wrap","the","original","efficientnet","to","an","encoder","for","flexible-unet","."]
docstr: Get number of efficientnet backbone output feature maps.
  Since every backbone contains the same 5 output feature maps,
  the number list should be `[5] * 10`.
docstr_tok: ["Get","number","of","efficientnet","backbone","output","feature","maps",".","Since","every","backbone","contains","the","same","5","output","feature","maps",",","the","number","list","should","be","`","[","5","]","*","10","`","."]
code: def num_outputs(cls) -> list[int]:
      """
      Get number of efficientnet backbone output feature maps.
      Since every backbone contains the same 5 output feature maps,
      the number list should be `[5] * 10`.
      """
      return [5] * 10
  
imports: from __future__ import annotations
  import math
  import operator
  import re
  from functools import reduce
  from typing import NamedTuple
  import torch
  from torch import nn
  from torch.utils import model_zoo
  from monai.networks.blocks import BaseEncoder
  from monai.networks.layers.factories import Act, Conv, Pad, Pool
  from monai.networks.layers.utils import get_norm_layer
  from monai.utils.module import look_up_option
label_desc: Use image node_id 3 for calling the EfficientNetEncoder obj's underlying member method code with example usage: obj.num_outputs(cls) and returns: unknown