class_docstr: Up-sampling the features by two using linear interpolation and convolutions.
class_docstr_tok: ["Up-sampling","the","features","by","two","using","linear","interpolation","and","convolutions","."]
docstr: Args:
      in_channel: number of input channels
      out_channel: number of output channels
      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","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,
      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
          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
      if self._spatial_dims not in (2, 3):
          raise ValueError("spatial_dims must be 2 or 3.")
  
      conv_type = Conv[Conv.CONV, self._spatial_dims]
      mode = "trilinear" if self._spatial_dims == 3 else "bilinear"
      self.add_module(
          "up",
          torch.nn.Upsample(
              scale_factor=2, mode=mode, align_corners=True
          ),
      )
      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=1,
              stride=1,
              padding=0,
              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 FactorizedIncreaseBlock object from inherited base classes: torch.nn with example: obj = FactorizedIncreaseBlock(in_channel, out_channel, spatial_dims, act_name, norm_name)