code: def __init__(
      self,
      in_channels=3,
      out_channels=3,
      down_block_types=(
          "DownEncoderBlock2D",
          "DownEncoderBlock2D",
          "DownEncoderBlock2D",
          "DownEncoderBlock2D",
      ),
      up_block_types=(
          "UpDecoderBlock2D",
          "UpDecoderBlock2D",
          "UpDecoderBlock2D",
          "UpDecoderBlock2D",
      ),
      block_out_channels=(128, 256, 512, 512),
      layers_per_block=2,
      act_fn="silu",
      latent_channels=4,
      sample_size=512,
  ):
      super().__init__()
  
      # pass init params to Encoder
      self.encoder = Encoder(
          in_channels=in_channels,
          out_channels=latent_channels,
          down_block_types=down_block_types,
          block_out_channels=block_out_channels,
          layers_per_block=layers_per_block,
          act_fn=act_fn,
          double_z=True,
      )
  
      # pass init params to Decoder
      self.decoder = Decoder(
          in_channels=latent_channels,
          out_channels=out_channels,
          up_block_types=up_block_types,
          block_out_channels=block_out_channels,
          layers_per_block=layers_per_block,
          act_fn=act_fn,
      )
  
      self.quant_conv = nn.Conv2D(
          2 * latent_channels, 2 * latent_channels, 1
      )
      self.post_quant_conv = nn.Conv2D(
          latent_channels, latent_channels, 1
      )
  
imports: import numpy
  import paddle
  import paddle.nn
  from ..configuration_utils import ConfigMixin
  from ..configuration_utils import register_to_config
  from .unet_blocks import get_down_block
  from .unet_blocks import get_up_block
  from .unet_blocks import UNetMidBlock2D
label_desc: Use image node_id 1 to create a new AutoencoderKL object from inherited base classes: nn, ConfigMixin with example: obj = AutoencoderKL(in_channels, out_channels, down_block_types, up_block_types, block_out_channels, layers_per_block, act_fn, latent_channels, sample_size)