class_docstr: Task for combined image-text vectorisation.
  
  Attributes:
      model:
          1. Model name
          2. Model name on Backprop's image-text-vectorisation endpoint
          3. Model object that implements the image-text-vectorisation task
      local (optional): Run locally. Defaults to False
      api_key (optional): Backprop API key for non-local inference
      device (optional): Device to run inference on. Defaults to "cuda" if available.
class_docstr_tok: ["Task","for","combined","image-text","vectorisation",".","Attributes",":","model",":","1",".","Model","name","2",".","Model","name","on","Backprop","'s","image-text-vectorisation","endpoint","3",".","Model","object","that","implements","the","image-text-vectorisation","task","local","(","optional",")",":","Run","locally",".","Defaults","to","False","api_key","(","optional",")",":","Backprop","API","key","for","non-local","inference","device","(","optional",")",":","Device","to","run","inference","on",".","Defaults","to","``","cuda","''","if","available","."]
docstr: Returns training dataloader with triplet loss sampling strategy.
docstr_tok: ["Returns","training","dataloader","with","triplet","loss","sampling","strategy","."]
code: def train_dataloader_triplet(self):
      """
      Returns training dataloader with triplet loss sampling strategy.
      """
      return DataLoader(
          self.dataset_train,
          batch_size=self.batch_size,
          num_workers=os.cpu_count() or 0,
          sampler=self.dl_sampler(self.dataset_train),
      )
  
imports: from typing import List, Tuple, Union, Dict
  from .base import Task
  from backprop.models import AutoModel, BaseModel
  from backprop.utils import path_to_img, img_to_base64
  from backprop.utils import ImageTextGroupDataset, base64_to_img, ImageTextPairDataset
  from backprop.utils.losses import TripletLoss
  from backprop.utils.samplers import SameGroupSampler
  import base64
  from PIL import Image
  from io import BytesIO
  import torch
  import random
  import requests
  import torch.nn.functional
  from torch.utils.data.dataloader import DataLoader
  import os
  import numpy
label_desc: Use image node_id 7 for calling the ImageTextVectorisation obj's underlying member method code with example usage: obj.train_dataloader_triplet() and returns: DataLoader