code: def _prediction_block(
      in_channels: int,
      out_channels: int,
      kernel_size: int,
      norm_layer: Callable[..., nn.Module],
  ) -> nn.Sequential:
      return nn.Sequential(
          # 3x3 depthwise with stride 1 and padding 1
          Conv2dNormActivation(
              in_channels,
              in_channels,
              kernel_size=kernel_size,
              groups=in_channels,
              norm_layer=norm_layer,
              activation_layer=nn.ReLU6,
          ),
          # 1x1 projetion to output channels
          nn.Conv2d(in_channels, out_channels, 1),
      )
  
imports: import warnings
  from collections import OrderedDict
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  from torch import nn, Tensor
  from ...ops.misc import Conv2dNormActivation
  from ...transforms._presets import ObjectDetection
  from ...utils import _log_api_usage_once
  from ..None import mobilenet
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights
  from .None import _utils
  from .anchor_utils import DefaultBoxGenerator
  from .backbone_utils import _validate_trainable_layers
  from .ssd import SSD, SSDScoringHead
label_desc: Use image node_id 1 for calling a global function with example usage: _prediction_block(in_channels, out_channels, kernel_size, norm_layer) and returns: nn