class_docstr: ALBEF VQA models.
  
  Supported model types:
      - base: vqa model initialized with pre-trained ALBEF base model on 115M image-text pairs after CapFilt; not fine-tuned.
      - vqav2: fine-tuned ALBEF base model on VQA v2.0 dataset.
  
  Usage:
      >>> from lavis.models import load_model
      >>> model = load_model("albef_vqa", "vqav2")
docstr: Args:
      samples (dict): A dictionary containing the following keys:
          - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W). Default H=480, W=480.
          - text_input (str or [str]): String or a list of strings, each string is a question.
                                       The number of questions must be equal to the batch size. If a single string, will be converted to a list of string, with length 1 first.
      num_ans_candidates (int): Number of answer candidates, used to filter out answers with low probability.
      answer_list (list): A list of strings, each string is an answer.
  
  Returns:
      List: A list of strings, each string is an answer.
  
  Examples:
      >>> from PIL import Image
      >>> from lavis.models import load_model_and_preprocess
      >>> model, vis_processors, txt_processors = load_model_and_preprocess("albef_vqa", "vqav2")
      >>> raw_image = Image.open("docs/data/merlion.png").convert("RGB")
      >>> question = "Which city is this photo taken?"
      >>> image = vis_processors["eval"](raw_image).unsqueeze(0)
      >>> question = txt_processors["eval"](question)
      >>> samples = {"image": image, "text_input": [question]}
      >>> answer_list = ["Singapore", "London", "Palo Alto", "Tokyo"]
      >>> answers = model.predict_answers(samples, answer_list=answer_list)
      >>> answers
      ['Singapore']
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code: def predict_answers(
      self, samples, answer_list, num_ans_candidates=128, **kwargs
  ):
      """
      Args:
          samples (dict): A dictionary containing the following keys:
              - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W). Default H=480, W=480.
              - text_input (str or [str]): String or a list of strings, each string is a question.
                                           The number of questions must be equal to the batch size. If a single string, will be converted to a list of string, with length 1 first.
          num_ans_candidates (int): Number of answer candidates, used to filter out answers with low probability.
          answer_list (list): A list of strings, each string is an answer.
  
      Returns:
          List: A list of strings, each string is an answer.
  
      Examples:
          >>> from PIL import Image
          >>> from lavis.models import load_model_and_preprocess
          >>> model, vis_processors, txt_processors = load_model_and_preprocess("albef_vqa", "vqav2")
          >>> raw_image = Image.open("docs/data/merlion.png").convert("RGB")
          >>> question = "Which city is this photo taken?"
          >>> image = vis_processors["eval"](raw_image).unsqueeze(0)
          >>> question = txt_processors["eval"](question)
          >>> samples = {"image": image, "text_input": [question]}
          >>> answer_list = ["Singapore", "London", "Palo Alto", "Tokyo"]
          >>> answers = model.predict_answers(samples, answer_list=answer_list)
          >>> answers
          ['Singapore']
      """
  
      if isinstance(samples["text_input"], str):
          samples["text_input"] = [samples["text_input"]]
  
      assert len(samples["text_input"]) == samples["image"].size(
          0
      ), "The number of questions must be equal to the batch size."
  
      num_ans_candidates = min(num_ans_candidates, len(answer_list))
  
      return self.rank_answers(
          samples,
          answer_list=answer_list,
          num_ans_candidates=num_ans_candidates,
      )
  
imports: import logging
  import os
  from copy import deepcopy
  import torch
  import torch.nn.functional
  from lavis.common.registry import registry
  from lavis.common.utils import get_abs_path, is_url
  from lavis.models.albef_models import AlbefBase
  from lavis.models.albef_models.albef_outputs import AlbefIntermediateOutput, AlbefOutput
  from lavis.models.base_model import MomentumDistilationMixin, tile
  from lavis.models.med import BertConfig, BertLMHeadModel, XBertEncoder
  from lavis.models.vit import VisionTransformerEncoder, interpolate_pos_embed
  from lavis.common.dist_utils import download_cached_file
label_desc: Use image node_id 6 for calling the AlbefVQA obj's underlying member method code with example usage: obj.predict_answers(samples, answer_list, num_ans_candidates) and returns: self