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 torch Tensor of classification prediction in shape ``(Batch, num_classes)``.
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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 torch Tensor of classification prediction in shape ``(Batch, num_classes)``.
      """
      # Stem
      x = self._conv_stem(self._conv_stem_padding(inputs))
      x = self._swish(self._bn0(x))
      # Blocks
      x = self._blocks(x)
      # Head
      x = self._conv_head(self._conv_head_padding(x))
      x = self._swish(self._bn1(x))
  
      # Pooling and final linear layer
      x = self._avg_pooling(x)
  
      x = x.flatten(start_dim=1)
      x = self._dropout(x)
      x = self._fc(x)
      return x
  
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 3 for calling the EfficientNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: x