code: def __init__(
      self,
      in_features: int,
      out_features: int,
      stride: int,
      use_bs: bool = True,
  ) -> None:
      super().__init__()
      self.use_bn = use_bs
      self.conv = nn.Conv2d(
          in_features, out_features, 1, stride=stride, bias=True
      )
      self.bn = nn.BatchNorm2d(out_features)
  
imports: from __future__ import annotations
  from collections import OrderedDict
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor, concatenate
  from kornia.core.check import KORNIA_CHECK
  from kornia.utils import map_location_to_cpu
label_desc: Use image node_id 1 to create a new SingleConvBlock object from inherited base classes: Module with example: obj = SingleConvBlock(in_features, out_features, stride, use_bs)