code: def __init__(self, config: LDMBertConfig):
      super().__init__()
      self.embed_dim = config.d_model
      self.self_attn = LDMBertAttention(
          embed_dim=self.embed_dim,
          num_heads=config.encoder_attention_heads,
          head_dim=config.head_dim,
          dropout=config.attention_dropout,
      )
      self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
      self.dropout = config.dropout
      self.activation_fn = ACT2FN[config.activation_function]
      self.activation_dropout = config.activation_dropout
      self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
      self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
      self.final_layer_norm = nn.LayerNorm(self.embed_dim)
  
imports: import inspect
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.utils.checkpoint
  from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutput
  from transformers.utils import logging
  from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
  from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 to create a new LDMBertEncoderLayer object from inherited base classes: nn with example: obj = LDMBertEncoderLayer(config)