class_docstr: Generates training and validation split indicies for a given dataset length and creates training and
  validation datasets using these splits
  
  Args:
      dataset_len: The length of the dataset to be split into training and validation
      split_fraction: The fraction of the whole dataset to be used for validation
      shuffle_seed: Optional random seed for the shuffling process
code: def __init__(self, dataset_len, split_fraction, shuffle_seed=None):
      self.dataset_len = dataset_len
      self.split_fraction = split_fraction
      self.valid_ids = None
      self.train_ids = None
      self._gen_split_ids(shuffle_seed)
  
file_path: torchbearer/torchbearer/cv_utils.py
imports: import math
  import torch
  from torch.utils.data import TensorDataset, Dataset
  import random
label_desc: Use image node_id 1 to create a new DatasetValidationSplitter object  with example: obj = DatasetValidationSplitter(dataset_len, split_fraction, shuffle_seed)