class_docstr: A simplified version of the atrous spatial pyramid pooling (ASPP) module.
  
  Chen et al., Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.
  https://arxiv.org/abs/1802.02611
  
  Wang et al., A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions
  from CT Images. https://ieeexplore.ieee.org/document/9109297
class_docstr_tok: ["A","simplified","version","of","the","atrous","spatial","pyramid","pooling","(","ASPP",")","module",".","Chen","et","al.",",","Encoder-Decoder","with","Atrous","Separable","Convolution","for","Semantic","Image","Segmentation",".","https",":","\/\/arxiv.org\/abs\/1802.02611","Wang","et","al.",",","A","Noise-robust","Framework","for","Automatic","Segmentation","of","COVID-19","Pneumonia","Lesions","from","CT","Images",".","https",":","\/\/ieeexplore.ieee.org\/document\/9109297"]
docstr: Args:
      x: in shape (batch, channel, spatial_1[, spatial_2, ...]).
docstr_tok: ["Args",":","x",":","in","shape","(","batch",",","channel",",","spatial_1","[",",","spatial_2",",","...","]",")","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x: in shape (batch, channel, spatial_1[, spatial_2, ...]).
      """
      x_out = torch.cat([conv(x) for conv in self.convs], dim=1)
      x_out = self.conv_k1(x_out)
      return x_out
  
file_path: MONAI/monai/networks/blocks/aspp.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.layers import same_padding
  from monai.networks.layers.factories import Conv
label_desc: Use image node_id 2 for calling the SimpleASPP obj's underlying member method code with example usage: obj.forward(x) and returns: x_out