code: def __init__(
      self,
      visual_encoder,
      text_encoder,
      hidden_size,
      num_classes,
      max_txt_len=40,
  ):
      super().__init__()
  
      self.tokenizer = self.init_tokenizer()
  
      self.visual_encoder = visual_encoder
  
      self.text_encoder = text_encoder
  
      if num_classes > 0:
          self.classifier = nn.Sequential(
              nn.Linear(hidden_size, hidden_size * 2),
              nn.ReLU(True),
              nn.Linear(hidden_size * 2, num_classes),
          )
      else:
          warn(
              f"num_classes is 0. Initialized {type(self)} without classifier."
          )
  
      self.max_txt_len = max_txt_len
  
file_path: lavis/lavis/models/alpro_models/alpro_qa.py
imports: from warnings import warn
  import torch
  import torch.nn.functional
  from lavis.common.config import node_to_dict
  from lavis.common.registry import registry
  from lavis.models.alpro_models import AlproBase
  from lavis.models.alpro_models.alpro_outputs import AlproIntermediateOutput, AlproOutputWithLogits
  from lavis.models.med import XBertEncoder
  from lavis.models.timesformer.vit import TimeSformer
  from torch import nn
label_desc: Use image node_id 1 to create a new AlproQA object from inherited base classes: AlproBase with example: obj = AlproQA(visual_encoder, text_encoder, hidden_size, num_classes, max_txt_len)