code: def __init__(
      self,
      text_decoder_name_or_path: str = "",
      tokenizer_name_or_path: str = "",
      image_size: int = 224,
      image_encoder_hidden_size: int = 768,
      image_encoder_patch_size: int = 16,
      image_encoder_num_layers: int = 12,
      image_encoder_num_heads: int = 12,
      image_encoder_embedding_dim: int = 256,
      image_encoder_pooling: str = "cls",
      image_pooler_num_attn_heads: int = 16,
      image_pooler_intermediate_size: int = 5504,
      image_pooler_num_latents: int = 196,
      image_token_id: int = 32002,
      initializer_range: float = 0.02,
      use_cache: bool = True,
      center_crop: bool = True,
      **kwargs,
  ):
      self.text_decoder_name_or_path = text_decoder_name_or_path
      self.tokenizer_name_or_path = tokenizer_name_or_path
  
      self.image_size = image_size
      self.image_encoder_hidden_size = image_encoder_hidden_size
      self.image_encoder_patch_size = image_encoder_patch_size
      self.image_encoder_num_layers = image_encoder_num_layers
      self.image_encoder_num_heads = image_encoder_num_heads
      self.image_encoder_embedding_dim = image_encoder_embedding_dim
      self.image_encoder_pooling = image_encoder_pooling
  
      self.image_pooler_num_attn_heads = image_pooler_num_attn_heads
      self.image_pooler_intermediate_size = (
          image_pooler_intermediate_size
      )
      self.image_pooler_num_latents = image_pooler_num_latents
  
      self.image_token_id = image_token_id
  
      self.initializer_range = initializer_range
      self.use_cache = use_cache
      self.center_crop = center_crop
  
      super().__init__(**kwargs)
  
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 VLMConfig object from inherited base classes: PretrainedConfig with example: obj = VLMConfig(text_decoder_name_or_path, tokenizer_name_or_path, image_size, image_encoder_hidden_size, image_encoder_patch_size, image_encoder_num_layers, image_encoder_num_heads, image_encoder_embedding_dim, image_encoder_pooling, image_pooler_num_attn_heads, image_pooler_intermediate_size, image_pooler_num_latents, image_token_id, initializer_range, use_cache, center_crop)