code: def __init__(
      self,
      block_out_channels: int,
      in_channels: int,
      use_bias: bool,
      ln_elementwise_affine: bool,
      layer_norm_eps: float,
      codebook_size: int,
  ):
      super().__init__()
      self.conv1 = nn.Conv2d(
          block_out_channels, in_channels, kernel_size=1, bias=use_bias
      )
      self.layer_norm = RMSNorm(
          in_channels, layer_norm_eps, ln_elementwise_affine
      )
      self.conv2 = nn.Conv2d(
          in_channels, codebook_size, kernel_size=1, bias=use_bias
      )
  
imports: from typing import Dict, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils.checkpoint import checkpoint
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..loaders import PeftAdapterMixin
  from .attention import BasicTransformerBlock, SkipFFTransformerBlock
  from .attention_processor import ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor
  from .embeddings import TimestepEmbedding, get_timestep_embedding
  from .modeling_utils import ModelMixin
  from .normalization import GlobalResponseNorm, RMSNorm
  from .resnet import Downsample2D, Upsample2D
label_desc: Use image node_id 1 to create a new ConvMlmLayer object from inherited base classes: nn with example: obj = ConvMlmLayer(block_out_channels, in_channels, use_bias, ln_elementwise_affine, layer_norm_eps, codebook_size)