class_docstr: Randomly splits items.
  
  # Arguments
      probs: `Sequence` of probabilities that must sum to one. The length of the
          `Sequence` is the number of groups to to split the items into.
      seed: Internal seed used for shuffling the items. Define this if you need
          reproducible results.
  
  # Examples
  
      Split data into three random groups.
  
      ```python
      idmap = IDMap(["file1", "file2", "file3", "file4"])
  
      data_splitter = RandomSplitter([0.6, 0.2, 0.2], seed=42)
      splits = data_splitter(idmap)
  
      np.testing.assert_equal(splits, [[1, 3], [0], [2]])
      ```
code: def __init__(self, probs: Sequence[int], seed: int = None):
      self.probs = probs
      self.seed = seed
  
imports: from icevision.imports import *
  from icevision.utils import *
  from icevision.core import *
label_desc: Use image node_id 1 to create a new RandomSplitter object from inherited base classes: DataSplitter with example: obj = RandomSplitter(probs, seed)