code: def conv1x1_bn_act(in_channels, out_channels, activation=nn.ReLU):
      return nn.Sequential(
          OrderedDict(
              [
                  (
                      "conv",
                      nn.Conv2d(
                          in_channels, out_channels, kernel_size=1
                      ),
                  ),
                  ("bn", nn.BatchNorm2d(out_channels)),
                  ("act", activation(inplace=True)),
              ]
          )
      )
  
imports: import inspect
  from collections import OrderedDict
  from typing import List, Callable, Tuple
  import torch
  from pytorch_toolbelt.modules import ACT_RELU, get_activation_block
  from pytorch_toolbelt.modules.encoders import EncoderModule, make_n_channel_input
  from torch import nn, Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: conv1x1_bn_act(in_channels, out_channels, activation) and returns: nn