code: def __call__(
      self, texts=None, images=None, return_tensors="pt", **kwargs
  ):
      if texts is not None:
          if isinstance(texts, str):
              texts = [texts]
  
          tokenized_texts = []
          for text in texts:
              messages = [
                  {
                      "role": "system",
                      "content": "You are a helpful assistant.",
                  },
                  {"role": "user", "content": f" <image> {text}"},
              ]
              tokenized_prompt = self.tokenizer.apply_chat_template(
                  messages,
                  add_generation_prompt=True,
                  return_tensors=return_tensors,
              )
  
              tokenized_texts.append(tokenized_prompt)
  
          max_len = max(len(t[0]) for t in tokenized_texts)
          input_ids = torch.full(
              (len(tokenized_texts), max_len),
              fill_value=self.tokenizer.pad_token_id,
              dtype=torch.int64,
          )
          attention_mask = torch.full(
              (len(tokenized_texts), max_len),
              fill_value=0,
              dtype=torch.int64,
          )
  
          for i, tokens in enumerate(tokenized_texts):
              input_ids[i, -len(tokens[0]) :] = tokens[0]
              attention_mask[i, -len(tokens[0]) :] = 1
  
          attention_mask = F.pad(
              attention_mask,
              pad=(0, self.num_image_latents - 1),
              value=1,
          )
  
          encoding = BatchEncoding(
              data={
                  "input_ids": input_ids,
                  "attention_mask": attention_mask,
              }
          )
  
      if images is not None:
          if isinstance(images, (list, tuple)):
              image_features = torch.empty(
                  (
                      len(images),
                      3,
                      self.config.image_size,
                      self.config.image_size,
                  ),
                  dtype=torch.float32,
              )
  
              for i, image in enumerate(images):
                  image_features[i] = self.image_processor(image)
          else:
              image_features = self.image_processor(images).unsqueeze(0)
  
      if texts is not None and images is not None:
          encoding["images"] = image_features
          return encoding
  
      if texts is not None:
          return encoding
  
      return BatchEncoding(
          data={
              "images": image_features,
          },
          tensor_type=return_tensors,
      )
  
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 2 for calling the VLMProcessor obj's underlying member method code with example usage: obj.__call__(texts, images, return_tensors) and returns: BatchEncoding, encoding, encoding