class_docstr: BLIP Image-Text Matching (ITM) model.
  Supported model types:
      - pretrained: pretrained model
      - coco: fintuned model on coco
  Usage:
      >>> from lavis.models import load_model
      >>> model = load_model("blip2_image_text_matching", "pretrained")
      >>> model = load_model("blip2_image_text_matching", "coco")
code: def __init__(
      self,
      vit_model="eva_clip_g",
      img_size=224,
      drop_path_rate=0,
      use_grad_checkpoint=False,
      vit_precision="fp16",
      freeze_vit=True,
      num_query_token=32,
      cross_attention_freq=2,
      embed_dim=256,
      max_txt_len=32,
  ):
      super().__init__(
          vit_model=vit_model,
          img_size=img_size,
          drop_path_rate=drop_path_rate,
          use_grad_checkpoint=use_grad_checkpoint,
          vit_precision=vit_precision,
          freeze_vit=freeze_vit,
          num_query_token=num_query_token,
          cross_attention_freq=cross_attention_freq,
          embed_dim=embed_dim,
          max_txt_len=max_txt_len,
      )
  
file_path: lavis/lavis/models/blip2_models/blip2_image_text_matching.py
imports: import torch
  import torch.nn.functional
  from lavis.common.registry import registry
  from lavis.models.blip2_models.blip2_qformer import Blip2Qformer
label_desc: Use image node_id 1 to create a new Blip2ITM object from inherited base classes: Blip2Qformer with example: obj = Blip2ITM(vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision, freeze_vit, num_query_token, cross_attention_freq, embed_dim, max_txt_len)