class_docstr: SegResEncoder based on the encoder structure in `3D MRI brain tumor segmentation using autoencoder regularization
  <https://arxiv.org/pdf/1810.11654.pdf>`_.
  
  Args:
      spatial_dims: spatial dimension of the input data. Defaults to 3.
      init_filters: number of output channels for initial convolution layer. Defaults to 32.
      in_channels: number of input channels for the network. Defaults to 1.
      out_channels: number of output channels for the network. Defaults to 2.
      act: activation type and arguments. Defaults to ``RELU``.
      norm: feature normalization type and arguments. Defaults to ``BATCH``.
      blocks_down: number of downsample blocks in each layer. Defaults to ``[1,2,2,4]``.
      head_module: optional callable module to apply to the final features.
      anisotropic_scales: optional list of scale for each scale level.
class_docstr_tok: ["SegResEncoder","based","on","the","encoder","structure","in","`","3D","MRI","brain","tumor","segmentation","using","autoencoder","regularization","<","https",":","\/\/arxiv.org\/pdf\/1810.11654.pdf",">","`","_",".","Args",":","spatial_dims",":","spatial","dimension","of","the","input","data",".","Defaults","to","3.","init_filters",":","number","of","output","channels","for","initial","convolution","layer",".","Defaults","to","32.","in_channels",":","number","of","input","channels","for","the","network",".","Defaults","to","1.","out_channels",":","number","of","output","channels","for","the","network",".","Defaults","to","2.","act",":","activation","type","and","arguments",".","Defaults","to","``","RELU","``",".","norm",":","feature","normalization","type","and","arguments",".","Defaults","to","``","BATCH","``",".","blocks_down",":","number","of","downsample","blocks","in","each","layer",".","Defaults","to","``","[","1,2,2,4","]","``",".","head_module",":","optional","callable","module","to","apply","to","the","final","features",".","anisotropic_scales",":","optional","list","of","scale","for","each","scale","level","."]
code: def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
      return self._forward(x)
  
imports: from __future__ import annotations
  from collections.abc import Callable
  from typing import Union
  import numpy
  import torch
  import torch.nn
  from monai.networks.blocks.upsample import UpSample
  from monai.networks.layers.factories import Act, Conv, Norm, split_args
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
  from monai.utils import UpsampleMode, has_option
label_desc: Use image node_id 3 for calling the SegResEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: self