class_docstr: -- (Acti) -- (Conv) -- (Norm) --
class_docstr_tok: ["--","(","Acti",")","--","(","Conv",")","--","(","Norm",")","--"]
docstr: Args:
      in_channel: number of input channels.
      out_channel: number of output channels.
      kernel_size: kernel size of the convolution.
      padding: padding size of the convolution.
      spatial_dims: number of spatial dimensions.
      act_name: activation layer type and arguments.
      norm_name: feature normalization type and arguments.
docstr_tok: ["Args",":","in_channel",":","number","of","input","channels",".","out_channel",":","number","of","output","channels",".","kernel_size",":","kernel","size","of","the","convolution",".","padding",":","padding","size","of","the","convolution",".","spatial_dims",":","number","of","spatial","dimensions",".","act_name",":","activation","layer","type","and","arguments",".","norm_name",":","feature","normalization","type","and","arguments","."]
code: def __init__(
      self,
      in_channel: int,
      out_channel: int,
      kernel_size: int = 3,
      padding: int = 1,
      spatial_dims: int = 3,
      act_name: tuple | str = "RELU",
      norm_name: tuple | str = ("INSTANCE", {"affine": True}),
  ):
      """
      Args:
          in_channel: number of input channels.
          out_channel: number of output channels.
          kernel_size: kernel size of the convolution.
          padding: padding size of the convolution.
          spatial_dims: number of spatial dimensions.
          act_name: activation layer type and arguments.
          norm_name: feature normalization type and arguments.
      """
      super().__init__()
      self._in_channel = in_channel
      self._out_channel = out_channel
      self._spatial_dims = spatial_dims
  
      conv_type = Conv[Conv.CONV, self._spatial_dims]
      self.add_module("acti", get_act_layer(name=act_name))
      self.add_module(
          "conv",
          conv_type(
              in_channels=self._in_channel,
              out_channels=self._out_channel,
              kernel_size=kernel_size,
              stride=1,
              padding=padding,
              groups=1,
              bias=False,
              dilation=1,
          ),
      )
      self.add_module(
          "norm",
          get_norm_layer(
              name=norm_name,
              spatial_dims=self._spatial_dims,
              channels=self._out_channel,
          ),
      )
  
file_path: MONAI/monai/networks/blocks/dints_block.py
imports: from __future__ import annotations
  import torch
  from monai.networks.layers.factories import Conv
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
label_desc: Use image node_id 1 to create a new ActiConvNormBlock object from inherited base classes: torch.nn with example: obj = ActiConvNormBlock(in_channel, out_channel, kernel_size, padding, spatial_dims, act_name, norm_name)