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
code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=3,
      stride=1,
      padding="",
      dilation=1,
      depthwise=False,
      **kwargs,
  ):
      super(MixedConv2d, self).__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: pytorch-image-models/timm/layers/mixed_conv2d.py
imports: import torch
  from torch import nn
  from .conv2d_same 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)