code: def forward(self, x: torch.Tensor):
      feature_list = []
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      if not self.no_max_pool:
          x = self.maxpool(x)
      x = self.layer1(x)
      feature_list.append(x)
      x = self.layer2(x)
      feature_list.append(x)
      x = self.layer3(x)
      feature_list.append(x)
      x = self.layer4(x)
      feature_list.append(x)
  
      return feature_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 7 for calling the ResNetEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: feature_list