class_docstr: Autoencoder Encoder model.
code: def __init__(self) -> None:
      super().__init__()
      self.enconv1 = nn.Conv2d(
          3, 32, kernel_size=4, stride=2, padding=1
      )
      self.enconv2 = nn.Conv2d(
          32, 32, kernel_size=4, stride=2, padding=1
      )
      self.enconv3 = nn.Conv2d(
          32, 64, kernel_size=4, stride=2, padding=1
      )
      self.enconv4 = nn.Conv2d(
          64, 64, kernel_size=4, stride=2, padding=1
      )
      self.enconv5 = nn.Conv2d(
          64, 64, kernel_size=4, stride=2, padding=1
      )
      self.enconv6 = nn.Conv2d(
          64, 64, kernel_size=8, stride=1, padding=0
      )
  
imports: from __future__ import annotations
  import logging
  import random
  from enum import Enum
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from torchvision import transforms
label_desc: Use image node_id 1 to create a new Encoder object from inherited base classes: nn with example: obj = Encoder()