code: def convolutional_stem(
      in_channels: int, out_channels: int, inference_mode: bool = False
  ) -> nn.Sequential:
      """Build convolutional stem with MobileOne blocks.
  
      Args:
          in_channels: Number of input channels.
          out_channels: Number of output channels.
          inference_mode: Flag to instantiate model in inference mode. Default: ``False``
  
      Returns:
          nn.Sequential object with stem elements.
      """
      return nn.Sequential(
          MobileOneBlock(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=3,
              stride=2,
              padding=1,
              groups=1,
              inference_mode=inference_mode,
              use_se=False,
              num_conv_branches=1,
          ),
          MobileOneBlock(
              in_channels=out_channels,
              out_channels=out_channels,
              kernel_size=3,
              stride=2,
              padding=1,
              groups=out_channels,
              inference_mode=inference_mode,
              use_se=False,
              num_conv_branches=1,
          ),
          MobileOneBlock(
              in_channels=out_channels,
              out_channels=out_channels,
              kernel_size=1,
              stride=1,
              padding=0,
              groups=1,
              inference_mode=inference_mode,
              use_se=False,
              num_conv_branches=1,
          ),
      )
  
imports: import os
  import copy
  from functools import partial
  from typing import List, Tuple, Optional, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.models.layers import DropPath, trunc_normal_
  from timm.models.registry import register_model
  from models.modules.mobileone import MobileOneBlock
  from models.modules.replknet import ReparamLargeKernelConv
label_desc: Use image node_id 2 for calling a global function with example usage: convolutional_stem(in_channels, out_channels, inference_mode) and returns: nn