class_docstr: Dtaset for loading image-text data for tasks like CLIP training, Image Captioning.
  
  Args:
      root: (string): The root path where the dataset is stored
      file_path: (string): Path to the file containing the image_paths and associated captions.
          The expected format is jsonlines where each line is a json object containing to keys.
          `image_path`: The path to the image.
          `captions`: An `array` of captions.
      transform (callable, optional): A function/transform that  takes in an PIL image
          and returns a transformed version. E.g, ``transforms.ToTensor``
      target_transform (callable, optional): A function/transform that takes in the
          target and transforms it.
      transforms (callable, optional): A function/transform that takes input sample and its target as entry
          and returns a transformed version.
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code: def _load_target(self, idx):
      return self.captions[idx]
  
imports: import json
  import logging
  import os
  import sys
  import time
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Callable, Optional
  import torch
  from torchvision.datasets import VisionDataset
  from torchvision.io import ImageReadMode, read_image
  from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
  from torchvision.transforms.functional import InterpolationMode
  from tqdm import tqdm
  import jax
  import jax.numpy
  import optax
  import transformers
  from flax import jax_utils
  from flax.jax_utils import unreplicate
  from flax.training import train_state
  from flax.training.common_utils import get_metrics, shard, shard_prng_key
  from modeling_hybrid_clip import FlaxHybridCLIP
  from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments, is_tensorboard_available, set_seed
label_desc: Use image node_id 3 for calling the ImageTextDataset obj's underlying member method code with example usage: obj._load_target(idx) and returns: self