code: def forward(self, x):  # noqa: F811
      if isinstance(x, torch.Tensor):
          prev_features = [x]
      else:
          prev_features = x
  
      if self.grad_checkpointing and self.any_requires_grad(
          prev_features
      ):
          if torch.jit.is_scripting():
              raise Exception("Memory Efficient not supported in JIT")
          bottleneck_output = self.call_checkpoint_bottleneck(
              prev_features
          )
      else:
          bottleneck_output = self.bottleneck_fn(prev_features)
  
      new_features = self.conv2(self.norm2(bottleneck_output))
      if self.drop_rate > 0:
          new_features = F.dropout(
              new_features, p=self.drop_rate, training=self.training
          )
      return new_features
  
imports: import re
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch.jit.annotations import List
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import BatchNormAct2d, get_norm_act_layer, BlurPool2d, create_classifier
  from ._builder import build_model_with_cfg
  from ._manipulate import MATCH_PREV_GROUP
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 7 for calling the DenseLayer obj's underlying member method code with example usage: obj.forward(x) and returns: new_features