code: def mnist_net(num_classes=10):
      return nn.Sequential(
          OrderedDict(
              [
                  ("conv", nn.Conv2d(1, 12, 3, padding=1)),
                  ("relu", nn.ReLU()),
                  ("pool", nn.MaxPool2d(2, stride=2, padding=0)),
                  ("flatten", nn.Flatten()),
                  ("dense", nn.Linear(2352, num_classes)),
                  ("softmax", nn.LogSoftmax()),
              ]
          )
      )
  
file_path: coremltools/docs/examples/optimize/torch/quantization/linear_quantization.py
imports: from collections import OrderedDict
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import os
  from torchvision import datasets, transforms
  from coremltools.optimize.torch.quantization import LinearQuantizer, LinearQuantizerConfig, ModuleLinearQuantizerConfig
  import coremltools
label_desc: Use image node_id 1 for calling a global function with example usage: mnist_net(num_classes) and returns: nn