docstr: Get parameter list to initialize encoder networks.
  Each parameter dict must have `spatial_dims`, `in_channels`
  and `pretrained` parameters.
docstr_tok: ["Get","parameter","list","to","initialize","encoder","networks",".","Each","parameter","dict","must","have","`","spatial_dims","`",",","`","in_channels","`","and","`","pretrained","`","parameters","."]
code: def get_encoder_parameters(cls) -> list[dict]:
      """
      Get parameter list to initialize encoder networks.
      Each parameter dict must have `spatial_dims`, `in_channels`
      and `pretrained` parameters.
      """
      parameter_list = []
      res_type: type[ResNetBlock] | type[ResNetBottleneck]
      for backbone in range(len(cls.backbone_names)):
          if backbone < 3:
              res_type = ResNetBlock
          else:
              res_type = ResNetBottleneck
          parameter_list.append(
              {
                  "block": res_type,
                  "layers": cls.parameter_layers[backbone],
                  "block_inplanes": ResNetEncoder.get_inplanes(),
                  "spatial_dims": 2,
                  "in_channels": 3,
                  "pretrained": False,
              }
          )
      return parameter_list
  
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.networks import eval_mode
  from monai.networks.blocks.encoder import BaseEncoder
  from monai.networks.nets import FLEXUNET_BACKBONE, EfficientNetBNFeatures, FlexibleUNet, FlexUNetEncoderRegister, ResNet, ResNetBlock, ResNetBottleneck
  from monai.utils import optional_import
  from tests.utils import skip_if_downloading_fails, skip_if_quick
label_desc: Use image node_id 3 for calling the ResNetEncoder obj's underlying member method code with example usage: obj.get_encoder_parameters(cls) and returns: parameter_list