class_docstr: Mixed Grouped Convolution
  
  Based on MDConv and GroupedConv in MixNet impl:
  https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
  
  Args:
      in_channels (`int`):
          Number of channels in the input image
      out_channels (`int`):
          Number of channels produced by the convolution.
      kernel_size (`int`):
          Size of the convolving kernel.
      stride (`int`):
          Stride of the convolution.
      padding (`str`):
          Padding added to all four sides of the input.
      dilation (`int`):
          Spacing between kernel elements.
      depthwise (`bool`):
          If True, use depthwise convolution.
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: int = 3,
      stride: int = 1,
      padding: str = "",
      dilation: int = 1,
      depthwise: bool = False,
      **kwargs,
  ) -> None:
      super().__init__()
  
      kernel_size = (
          kernel_size
          if isinstance(kernel_size, list)
          else [kernel_size]
      )
      num_groups = len(kernel_size)
      in_splits = _split_channels(in_channels, num_groups)
      out_splits = _split_channels(out_channels, num_groups)
      self.in_channels = sum(in_splits)
      self.out_channels = sum(out_splits)
      for idx, (k, in_ch, out_ch) in enumerate(
          zip(kernel_size, in_splits, out_splits)
      ):
          conv_groups = in_ch if depthwise else 1
          # use add_module to keep key space clean
          self.add_module(
              str(idx),
              create_conv2d_pad(
                  in_ch,
                  out_ch,
                  k,
                  stride=stride,
                  padding=padding,
                  dilation=dilation,
                  groups=conv_groups,
                  **kwargs,
              ),
          )
      self.splits = in_splits
  
file_path: towhee/towhee/models/layers/mixed_conv2d.py
imports: from typing import List
  import torch
  from torch import nn
  from towhee.models.utils.create_conv2d_pad import create_conv2d_pad
label_desc: Use image node_id 1 to create a new MixedConv2d object from inherited base classes: nn with example: obj = MixedConv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, depthwise)