class_docstr: Dataset class to provide (augmented) correctly prepared training samples corresponding to standard DML literature.
  This includes normalizing to ImageNet-standards, and Random & Resized cropping of shapes 224 for ResNet50 and 227 for
  GoogLeNet during Training. During validation, only resizing to 256 or center cropping to 224/227 is performed.
docstr: Function that ensures that the input img is three-dimensional.
  
  Args:
      img: PIL.Image, image which is to be checked for three-dimensionality (i.e. if some images are black-and-white in an otherwise coloured dataset).
  Returns:
      Checked PIL.Image img.
docstr_tok: ["Function","that","ensures","that","the","input","img","is","three-dimensional",".","Args",":","img",":","PIL.Image",",","image","which","is","to","be","checked","for","three-dimensionality","(","i.e",".","if","some","images","are","black-and-white","in","an","otherwise","coloured","dataset",")",".","Returns",":","Checked","PIL.Image","img","."]
code: def ensure_3dim(self, img):
      """
      Function that ensures that the input img is three-dimensional.
  
      Args:
          img: PIL.Image, image which is to be checked for three-dimensionality (i.e. if some images are black-and-white in an otherwise coloured dataset).
      Returns:
          Checked PIL.Image img.
      """
      if len(img.size) == 2:
          img = img.convert("RGB")
      return img
  
imports: import pickle
  import warnings
  from numpy.core.arrayprint import IntegerFormat
  import copy
  import os
  import random
  import numpy
  import pandas
  import torch
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
label_desc: Use image node_id 2 for calling the BaseTripletDataset obj's underlying member method code with example usage: obj.ensure_3dim(img) and returns: img