class_docstr: Base class for multi-modal image-text data
docstr: Add model specific arguments
docstr_tok: ["Add","model","specific","arguments"]
code: def add_arguments(cls, parser: argparse.ArgumentParser):
      """Add model specific arguments"""
      group = parser.add_argument_group(title=cls.__name__)
      group.add_argument(
          "--model.multi-modal-image-text.clip.projection-dim",
          type=int,
          default=256,
          help="Project image and text features to this dimensionality",
      )
      group.add_argument(
          "--model.multi-modal-image-text.clip.cache-text-features-zero-shot",
          action="store_true",
          help="Cache text features for zero-shot during inference",
      )
  
      return parser
  
imports: import argparse
  import math
  from typing import Dict, Optional
  import torch
  from torch import Tensor, nn
  from cvnets.image_projection_layers import build_image_projection_head
  from cvnets.image_projection_layers.base_image_projection import get_in_feature_dimension
  from cvnets.models import MODEL_REGISTRY, BaseAnyNNModel, get_model
  from cvnets.models.classification.base_image_encoder import BaseImageEncoder
  from cvnets.models.multi_modal_img_text.base_multi_modal_img_text import BaseMultiModalImageText
  from cvnets.text_encoders import BaseTextEncoder, build_text_encoder
  from utils import logger
label_desc: Use image node_id 2 for calling the CLIP obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser