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
docstr: Creates a training dataset from existing dataset
  
  Args:
      dataset (torch.utils.data.Dataset): Dataset to be split into a training dataset
  
  Returns:
      torch.utils.data.Dataset: Training dataset split from whole dataset
docstr_tok: ["Creates","a","training","dataset","from","existing","dataset","Args",":","dataset","(","torch.utils.data.Dataset",")",":","Dataset","to","be","split","into","a","training","dataset","Returns",":","torch.utils.data.Dataset",":","Training","dataset","split","from","whole","dataset"]
code: def get_train_dataset(self, dataset):
      """Creates a training dataset from existing dataset
  
      Args:
          dataset (torch.utils.data.Dataset): Dataset to be split into a training dataset
  
      Returns:
          torch.utils.data.Dataset: Training dataset split from whole dataset
      """
      return SubsetDataset(dataset, self.train_ids)
  
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 3 for calling the DatasetValidationSplitter obj's underlying member method code with example usage: obj.get_train_dataset(dataset) and returns: SubsetDataset