docstr: MBConvBlock"s forward function.
  
  Args:
      inputs: Input tensor.
  
  Returns:
      Output of this block after processing.
docstr_tok: ["MBConvBlock","''","s","forward","function",".","Args",":","inputs",":","Input","tensor",".","Returns",":","Output","of","this","block","after","processing","."]
code: def forward(self, inputs: torch.Tensor):
      """MBConvBlock"s forward function.
  
      Args:
          inputs: Input tensor.
  
      Returns:
          Output of this block after processing.
      """
      # Expansion and Depthwise Convolution
      x = inputs
      if self.expand_ratio != 1:
          x = self._expand_conv(self._expand_conv_padding(x))
          x = self._bn0(x)
          x = self._swish(x)
  
      x = self._depthwise_conv(self._depthwise_conv_padding(x))
      x = self._bn1(x)
      x = self._swish(x)
  
      # Squeeze and Excitation
      if self.has_se:
          x_squeezed = self._se_adaptpool(x)
          x_squeezed = self._se_reduce(
              self._se_reduce_padding(x_squeezed)
          )
          x_squeezed = self._swish(x_squeezed)
          x_squeezed = self._se_expand(
              self._se_expand_padding(x_squeezed)
          )
          x = torch.sigmoid(x_squeezed) * x
  
      # Pointwise Convolution
      x = self._project_conv(self._project_conv_padding(x))
      x = self._bn2(x)
  
      # Skip connection and drop connect
      if (
          self.id_skip
          and self.stride == 1
          and self.in_channels == self.out_channels
      ):
          # the combination of skip connection and drop connect brings about stochastic depth.
          if self.drop_connect_rate:
              x = drop_connect(
                  x, p=self.drop_connect_rate, training=self.training
              )
          x = x + inputs  # skip connection
      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 2 for calling the MBConvBlock obj's underlying member method code with example usage: obj.forward(inputs) and returns: x