class_docstr: Encoder part of the reconstructive network.
  
  Args:
      in_channels (int): Number of input channels.
      base_width (int): Base dimensionality of the layers of the autoencoder.
class_docstr_tok: ["Encoder","part","of","the","reconstructive","network",".","Args",":","in_channels","(","int",")",":","Number","of","input","channels",".","base_width","(","int",")",":","Base","dimensionality","of","the","layers","of","the","autoencoder","."]
docstr: Encode a batch of input images to the salient space.
  
  Args:
      batch (Tensor): Batch of input images.
  
  Returns:
      Feature maps extracted from the bottleneck layer.
docstr_tok: ["Encode","a","batch","of","input","images","to","the","salient","space",".","Args",":","batch","(","Tensor",")",":","Batch","of","input","images",".","Returns",":","Feature","maps","extracted","from","the","bottleneck","layer","."]
code: def forward(self, batch: Tensor) -> Tensor:
      """Encode a batch of input images to the salient space.
  
      Args:
          batch (Tensor): Batch of input images.
  
      Returns:
          Feature maps extracted from the bottleneck layer.
      """
      act1 = self.block1(batch)
      mp1 = self.mp1(act1)
      act2 = self.block2(mp1)
      mp2 = self.mp3(act2)
      act3 = self.block3(mp2)
      mp3 = self.mp3(act3)
      act4 = self.block4(mp3)
      mp4 = self.mp4(act4)
      act5 = self.block5(mp4)
      return act5
  
imports: from __future__ import annotations
  import torch
  from torch import Tensor, nn
  from anomalib.models.components.layers import SSPCAB
label_desc: Use image node_id 2 for calling the EncoderReconstructive obj's underlying member method code with example usage: obj.forward(batch) and returns: act5