class_docstr: Base class for multi-modal image-text data
docstr: Create dummy input and labels for CI/CD purposes. Child classes must override it
  if functionality is different.
docstr_tok: ["Create","dummy","input","and","labels","for","CI\/CD","purposes",".","Child","classes","must","override","it","if","functionality","is","different","."]
code: def dummy_input_and_label(self, batch_size: int) -> Dict:
      """Create dummy input and labels for CI/CD purposes. Child classes must override it
      if functionality is different.
      """
      img_channels = 3
      height = 224
      width = 224
      vocab_size = 10
      seq_length = 5
      img_tensor = torch.randn(
          batch_size, img_channels, height, width, dtype=torch.float
      )
  
      text_tensor = torch.randint(
          low=0, high=vocab_size, size=(batch_size, seq_length)
      ).long()
  
      return {
          "samples": {"image": img_tensor, "text": text_tensor},
          "targets": text_tensor,
      }
  
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 5 for calling the CLIP obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: dict