text: import torch
  from torch.nn import Conv2d, Sequential, ModuleList, BatchNorm2d
  from torch import nn
  from ..nn.mobilenet_v2 import MobileNetV2, InvertedResidual
  
  from .ssd import SSD, GraphPath
  from .predictor import Predictor
  from .config import mobilenetv1_ssd_config as config
  
  
  def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, onnx_compatible=False):
      """Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.
      """
      ReLU = nn.ReLU if onnx_compatible else nn.ReLU6
      return Sequential(
          Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,
                 groups=in_channels, stride=stride, padding=padding),
          BatchNorm2d(in_channels),
          ReLU(),
          Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),
      )
  
  
  def create_mobilenetv2_ssd_lite(num_classes, width_mult=1.0, use_batch_norm=True, onnx_compatible=False, is_test=False):
      base_net = MobileNetV2(width_mult=width_mult, use_batch_norm=use_batch_norm,
                             onnx_compatible=onnx_compatible).features
  
      source_layer_indexes = [
          GraphPath(14, 'conv', 3),
          19,
      ]
      extras = ModuleList([
          InvertedResidual(1280, 512, stride=2, expand_ratio=0.2),
          InvertedResidual(512, 256, stride=2, expand_ratio=0.25),
          InvertedResidual(256, 256, stride=2, expand_ratio=0.5),
          InvertedResidual(256, 64, stride=2, expand_ratio=0.25)
      ])
  
      regression_headers = ModuleList([
          SeperableConv2d(in_channels=round(576 * width_mult), out_channels=6 * 4,
                          kernel_size=3, padding=1, onnx_compatible=False),
          SeperableConv2d(in_channels=1280, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
          SeperableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
          SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
          SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
          Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1),
      ])
  
      classification_headers = ModuleList([
          SeperableConv2d(in_channels=round(576 * width_mult), out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=1280, out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
          SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
          Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1),
      ])
  
      return SSD(num_classes, base_net, source_layer_indexes,
                 extras, classification_headers, regression_headers, is_test=is_test, config=config, device=torch.device('cpu'))
  
  
  def create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=torch.device('cpu')):
      predictor = Predictor(net, config.image_size, config.image_mean,
                            config.image_std,
                            nms_method=nms_method,
                            iou_threshold=config.iou_threshold,
                            candidate_size=candidate_size,
                            sigma=sigma,
                            device=device)
      return predictor
  