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 forward(self, x):
      x_split = torch.split(x, self.splits, 1)
      x_out = [conv(x_split[i]) for i, conv in enumerate(self.values())]
      x = torch.cat(x_out, 1)
      return x
  
imports: import collections.abc
  import math
  from functools import partial
  from itertools import repeat
  from typing import Tuple, Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from .config import *
label_desc: Use image node_id 2 for calling the MixedConv2d obj's underlying member method code with example usage: obj.forward(x) and returns: x