class_docstr: Keras compatible data generator that wraps a Keras Sequence and provides corrupted
  data for training Deep Graph Infomax.
  
  Args:
      base_sequence: the uncorrupted Sequence object.
      corrupt_index_groups: the groups among which nodes will be shuffled (see :class:`.CorruptedGenerator` for more details)
      num_batch_dims: the number of axes that are "batch" dimensions
class_docstr_tok: ["Keras","compatible","data","generator","that","wraps","a","Keras","Sequence","and","provides","corrupted","data","for","training","Deep","Graph","Infomax",".","Args",":","base_sequence",":","the","uncorrupted","Sequence","object",".","corrupt_index_groups",":","the","groups","among","which","nodes","will","be","shuffled","(","see",":","class",":","`",".CorruptedGenerator","`","for","more","details",")","num_batch_dims",":","the","number","of","axes","that","are","``","batch","''","dimensions"]
code: def __init__(
      self, base_sequence, corrupt_index_groups, num_batch_dims
  ):
      self.corrupt_index_groups = corrupt_index_groups
      self.base_sequence = base_sequence
      self.num_batch_dims = num_batch_dims
  
file_path: stellargraph/stellargraph/mapper/corrupted.py
imports: import numpy
  from tensorflow.keras.utils import Sequence
  from .None import Generator
  from ..core.validation import comma_sep
label_desc: Use image node_id 1 to create a new CorruptedSequence object from inherited base classes: Sequence with example: obj = CorruptedSequence(base_sequence, corrupt_index_groups, num_batch_dims)