class_docstr: A feature-extraction module that can be used for LocalNet, based on:
  `Weakly-supervised convolutional neural networks for multimodal image registration
  <https://doi.org/10.1016/j.media.2018.07.002>`_.
  `Label-driven weakly-supervised learning for multimodal deformable image registration
  <https://arxiv.org/abs/1711.01666>`_.
  
  Adapted from:
      DeepReg (https://github.com/DeepRegNet/DeepReg)
class_docstr_tok: ["A","feature-extraction","module","that","can","be","used","for","LocalNet",",","based","on",":","`","Weakly-supervised","convolutional","neural","networks","for","multimodal","image","registration","<","https",":","\/\/doi.org\/10.1016\/j.media.2018.07.002",">","`","_",".","`","Label-driven","weakly-supervised","learning","for","multimodal","deformable","image","registration","<","https",":","\/\/arxiv.org\/abs\/1711.01666",">","`","_",".","Adapted","from",":","DeepReg","(","https",":","\/\/github.com\/DeepRegNet\/DeepReg",")"]
docstr: Args:
      x: Tensor in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])
docstr_tok: ["Args",":","x",":","Tensor","in","shape","(","batch",",","``","in_channels","``",",","insize_1",",","insize_2",",","[","insize_3","]",")"]
code: def forward(self, x) -> torch.Tensor:
      """
      Args:
          x: Tensor in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])
      """
      out: torch.Tensor = self.conv_block(x)
      return out
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  from torch import nn
  from torch.nn import functional
  from monai.networks.blocks import Convolution
  from monai.networks.layers import same_padding
  from monai.networks.layers.factories import Conv, Norm, Pool
label_desc: Use image node_id 2 for calling the LocalNetFeatureExtractorBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out