class_docstr: Base class for multi-modal image-text data
code: def _exponentiate_and_clip_logits(self, max_scale: float = 100.0):
      scale = self.logit_scale.exp()
      scale = torch.clamp(scale, 0, max_scale)
      return scale
  
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 6 for calling the CLIP obj's underlying member method code with example usage: obj._exponentiate_and_clip_logits(max_scale) and returns: scale