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 the initialization parameter for efficientnet backbones.
docstr_tok: ["Get","the","initialization","parameter","for","efficientnet","backbones","."]
code: def get_encoder_parameters(cls) -> list[dict]:
      """
      Get the initialization parameter for efficientnet backbones.
      """
      parameter_list = []
      for backbone_name in cls.backbone_names:
          parameter_list.append(
              {
                  "model_name": backbone_name,
                  "pretrained": True,
                  "progress": True,
                  "spatial_dims": 2,
                  "in_channels": 3,
                  "num_classes": 1000,
                  "norm": ("batch", {"eps": 1e-3, "momentum": 0.01}),
                  "adv_prop": "ap" in backbone_name,
              }
          )
      return parameter_list
  
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 1 for calling the EfficientNetEncoder obj's underlying member method code with example usage: obj.get_encoder_parameters(cls) and returns: parameter_list