class_docstr: Encoder part of the discriminator network.
  
  Args:
      in_channels (int): Number of input channels.
      base_width (int): Base dimensionality of the layers of the autoencoder.
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docstr: Convert the inputs to the salient space by running them through the encoder network.
  
  Args:
      batch (Tensor): Batch of inputs consisting of the concatenation of the original images
       and their reconstructions.
  
  Returns:
      Computed feature maps for each of the layers in the encoder sub network.
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code: def forward(
      self, batch: Tensor
  ) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
      """Convert the inputs to the salient space by running them through the encoder network.
  
      Args:
          batch (Tensor): Batch of inputs consisting of the concatenation of the original images
           and their reconstructions.
  
      Returns:
          Computed feature maps for each of the layers in the encoder sub network.
      """
      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)
      mp5 = self.mp5(act5)
      act6 = self.block6(mp5)
      return act1, act2, act3, act4, act5, act6
  
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 EncoderDiscriminative obj's underlying member method code with example usage: obj.forward(batch) and returns: act1,  act2,  act3,  act4,  act5,  act6