code: def __init__(self, in_channels, pretrained, **kargs):
      super().__init__(**kargs, n_input_channels=in_channels)
      if pretrained:
          # Author of paper zipped the state_dict on googledrive,
          # so would need to download, unzip and read (2.8gb file for a ~150mb state dict).
          # Would like to load dict from url but need somewhere to save the state dicts.
          raise NotImplementedError(
              "Currently not implemented. You need to manually download weights provided by the paper's author"
              " and load then to the model with `state_dict`. See https://github.com/Tencent/MedicalNet"
          )
  
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 1 to create a new ResNetEncoder object from inherited base classes: ResNet, BaseEncoder with example: obj = ResNetEncoder(in_channels, pretrained)