docstr: Args:
      inputs: input should have spatially N dimensions
      ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`.
  
  Returns:
      a list of torch Tensors.
docstr_tok: ["Args",":","inputs",":","input","should","have","spatially","N","dimensions","``","(","Batch",",","in_channels",",","dim_0","[",",","dim_1",",","...",",","dim_N","]",")","``",",","N","is","defined","by","`","dimensions","`",".","Returns",":","a","list","of","torch","Tensors","."]
code: def forward(self, inputs: torch.Tensor):
      """
      Args:
          inputs: input should have spatially N dimensions
          ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`.
  
      Returns:
          a list of torch Tensors.
      """
      # Stem
      x = self._conv_stem(self._conv_stem_padding(inputs))
      x = self._swish(self._bn0(x))
  
      features = []
      if 0 in self.extract_stacks:
          features.append(x)
      for i, block in enumerate(self._blocks):
          x = block(x)
          if i + 1 in self.extract_stacks:
              features.append(x)
      return features
  
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 2 for calling the EfficientNetBNFeatures obj's underlying member method code with example usage: obj.forward(inputs) and returns: features