class_docstr: maxpooling downsampling and two convolutions.
class_docstr_tok: ["maxpooling","downsampling","and","two","convolutions","."]
docstr: Args:
      spatial_dims: number of spatial dimensions.
      in_chns: number of input channels.
      out_chns: number of output channels.
      act: activation type and arguments.
      norm: feature normalization type and arguments.
      bias: whether to have a bias term in convolution blocks.
      dropout: dropout ratio. Defaults to no dropout.
docstr_tok: ["Args",":","spatial_dims",":","number","of","spatial","dimensions",".","in_chns",":","number","of","input","channels",".","out_chns",":","number","of","output","channels",".","act",":","activation","type","and","arguments",".","norm",":","feature","normalization","type","and","arguments",".","bias",":","whether","to","have","a","bias","term","in","convolution","blocks",".","dropout",":","dropout","ratio",".","Defaults","to","no","dropout","."]
code: def __init__(
      self,
      spatial_dims: int,
      in_chns: int,
      out_chns: int,
      act: str | tuple,
      norm: str | tuple,
      bias: bool,
      dropout: float | tuple = 0.0,
  ):
      """
      Args:
          spatial_dims: number of spatial dimensions.
          in_chns: number of input channels.
          out_chns: number of output channels.
          act: activation type and arguments.
          norm: feature normalization type and arguments.
          bias: whether to have a bias term in convolution blocks.
          dropout: dropout ratio. Defaults to no dropout.
  
      """
      super().__init__()
      max_pooling = Pool["MAX", spatial_dims](kernel_size=2)
      convs = TwoConv(
          spatial_dims, in_chns, out_chns, act, norm, bias, dropout
      )
      self.add_module("max_pooling", max_pooling)
      self.add_module("convs", convs)
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from typing import Optional
  import torch
  import torch.nn
  from monai.networks.blocks import Convolution, UpSample
  from monai.networks.layers.factories import Conv, Pool
  from monai.utils import ensure_tuple_rep
label_desc: Use image node_id 1 to create a new Down object from inherited base classes: nn with example: obj = Down(spatial_dims, in_chns, out_chns, act, norm, bias, dropout)