docstr: Args:
      in_channels: number of the input channel.
      act: activation type and arguments. Defaults to relu.
      norm: feature normalization type and arguments. Defaults to batch norm.
docstr_tok: ["Args",":","in_channels",":","number","of","the","input","channel",".","act",":","activation","type","and","arguments",".","Defaults","to","relu",".","norm",":","feature","normalization","type","and","arguments",".","Defaults","to","batch","norm","."]
code: def __init__(
      self,
      in_channels: int,
      act: str | tuple = ("relu", {"inplace": True}),
      norm: str | tuple = "batch",
  ) -> None:
      """
      Args:
          in_channels: number of the input channel.
          act: activation type and arguments. Defaults to relu.
          norm: feature normalization type and arguments. Defaults to batch norm.
      """
      super().__init__()
  
      self.add_module(
          "bn",
          get_norm_layer(
              name=norm, spatial_dims=2, channels=in_channels
          ),
      )
      self.add_module("relu", get_act_layer(name=act))
  
imports: from __future__ import annotations
  import os
  import re
  import warnings
  from collections import OrderedDict
  from collections.abc import Callable, Sequence
  import torch
  import torch.nn
  from monai.apps.utils import download_url
  from monai.networks.blocks import UpSample
  from monai.networks.layers.factories import Conv, Dropout
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
  from monai.utils.enums import HoVerNetBranch, HoVerNetMode, InterpolateMode, UpsampleMode
  from monai.utils.module import export, look_up_option
label_desc: Use image node_id 1 to create a new _Transition object from inherited base classes: nn with example: obj = _Transition(in_channels, act, norm)