code: def depthwise_conv(
      input_c: int,
      output_c: int,
      kernel_s: int,
      stride: int = 1,
      padding: int = 0,
      bias: bool = False,
  ) -> nn.Conv2d:
      return nn.Conv2d(
          in_channels=input_c,
          out_channels=output_c,
          kernel_size=kernel_s,
          stride=stride,
          padding=padding,
          bias=bias,
          groups=input_c,
      )
  
file_path: deep-learning-for-image-processing/pytorch_classification/mini_imagenet/model.py
imports: from typing import List, Callable
  import torch
  from torch import Tensor
  import torch.nn
label_desc: Use image node_id 2 for calling the InvertedResidual obj's underlying member method code with example usage: obj.depthwise_conv(input_c, output_c, kernel_s, stride, padding, bias) and returns: nn