code: def __init__(self, config: VLMConfig):
      super().__init__(config)
  
      self.config = config
      self.text_config = AutoConfig.from_pretrained(
          config.text_decoder_name_or_path
      )
      self.text_config.vocab_size += 3
      self.text_decoder = AutoModelForCausalLM.from_config(
          self.text_config
      )
  
      self.image_encoder = VisualEncoder(
          self.config.image_encoder_hidden_size,
          self.config.image_encoder_patch_size,
          self.config.image_size,
          self.config.image_encoder_num_layers,
          self.config.image_encoder_num_heads,
          self.config.image_encoder_embedding_dim,
          self.config.image_encoder_pooling,
      )
  
      # replace models' layerscales because `transformers` automatically renames keys in state_dict
      for i in range(len(self.image_encoder.blocks)):
          self.image_encoder.blocks[i].ls1 = LayerScale(
              self.image_encoder.blocks[i].ls1.dim
          )
          self.image_encoder.blocks[i].ls2 = LayerScale(
              self.image_encoder.blocks[i].ls2.dim
          )
  
      self.image_pooler = ImageFeaturesPooler(
          self.config.image_encoder_hidden_size,
          self.text_config.hidden_size,
          self.config.image_pooler_num_attn_heads,
          self.config.image_pooler_intermediate_size,
          self.config.image_pooler_num_latents,
          self.config.initializer_range,
      )
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torchvision.transforms import *
  from transformers import AutoConfig, AutoTokenizer
  from transformers.configuration_utils import PretrainedConfig
  from transformers.modeling_outputs import CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.auto.modeling_auto import AutoModel, AutoModelForCausalLM
  from transformers.processing_utils import ProcessorMixin
  from transformers.tokenization_utils_base import BatchEncoding
  from uform.models import VisualEncoder
label_desc: Use image node_id 1 to create a new VLMForCausalLM object from inherited base classes: VLMPreTrainedModel with example: obj = VLMForCausalLM(config)