code: def __init__(
      self,
      vocab_size=30522,
      max_position_embeddings=77,
      encoder_layers=32,
      encoder_ffn_dim=5120,
      encoder_attention_heads=8,
      head_dim=64,
      encoder_layerdrop=0.0,
      activation_function="gelu",
      d_model=1280,
      dropout=0.1,
      attention_dropout=0.0,
      activation_dropout=0.0,
      init_std=0.02,
      classifier_dropout=0.0,
      scale_embedding=False,
      use_cache=True,
      pad_token_id=0,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.d_model = d_model
      self.encoder_ffn_dim = encoder_ffn_dim
      self.encoder_layers = encoder_layers
      self.encoder_attention_heads = encoder_attention_heads
      self.head_dim = head_dim
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.activation_function = activation_function
      self.init_std = init_std
      self.encoder_layerdrop = encoder_layerdrop
      self.classifier_dropout = classifier_dropout
      self.use_cache = use_cache
      self.num_hidden_layers = encoder_layers
      self.scale_embedding = (
          scale_embedding  # scale factor will be sqrt(d_model) if True
      )
  
      super().__init__(pad_token_id=pad_token_id, **kwargs)
  
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 LDMBertConfig object from inherited base classes: PretrainedConfig with example: obj = LDMBertConfig(vocab_size, max_position_embeddings, encoder_layers, encoder_ffn_dim, encoder_attention_heads, head_dim, encoder_layerdrop, activation_function, d_model, dropout, attention_dropout, activation_dropout, init_std, classifier_dropout, scale_embedding, use_cache, pad_token_id)