class_docstr: Down-sampling the feature by 2 using stride.
  The length along each spatial dimension must be a multiple of 2.
class_docstr_tok: ["Down-sampling","the","feature","by","2","using","stride",".","The","length","along","each","spatial","dimension","must","be","a","multiple","of","2","."]
docstr: The length along each spatial dimension must be a multiple of 2.
docstr_tok: ["The","length","along","each","spatial","dimension","must","be","a","multiple","of","2","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      The length along each spatial dimension must be a multiple of 2.
      """
      x = self.act(x)
      if self._spatial_dims == 3:
          out = torch.cat(
              [self.conv_1(x), self.conv_2(x[:, :, 1:, 1:, 1:])], dim=1
          )
      else:
          out = torch.cat(
              [self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1
          )
      out = self.norm(out)
      return out
  
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 2 for calling the FactorizedReduceBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out