code: def convolutional_stem(
      opts: argparse.Namespace, in_channels: int, out_channels: int
  ) -> nn.Sequential:
      """
      Build convolutional stem with MobileOne blocks.
  
      Args:
          opts: Command line arguments.
          in_channels: Number of input channels.
          out_channels: Number of output channels.
  
      Returns:
          nn.Sequential object with stem elements.
      """
      inference_mode = getattr(
          opts, "model.classification.fastvit.inference_mode"
      )
      return nn.Sequential(
          MobileOneBlock(
              opts,
              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(
              opts,
              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(
              opts,
              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 argparse
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from cvnets.layers import ConvLayer2d, MultiHeadAttention, StochasticDepth
  from cvnets.layers.normalization.batch_norm import BatchNorm2d
  from cvnets.modules import BaseModule
  from cvnets.modules.mobileone_block import MobileOneBlock, RepLKBlock
label_desc: Use image node_id 1 for calling a global function with example usage: convolutional_stem(opts, in_channels, out_channels) and returns: nn