class_docstr: A negative sampler used to generate negative samples and return
  a mix of positive and negative samples.
  
  Functional name: :obj:`sample_negative`.
  
  Parameters
  ----------
  datapipe : DataPipe
      The datapipe.
  negative_ratio : int
      The proportion of negative samples to positive samples.
code: def __init__(
      self,
      datapipe,
      negative_ratio,
  ):
      super().__init__(datapipe, self._sample)
      assert (
          negative_ratio > 0
      ), "Negative_ratio should be positive Integer."
      self.negative_ratio = negative_ratio
  
file_path: dgl/python/dgl/graphbolt/negative_sampler.py
imports: from _collections_abc import Mapping
  from torch.utils.data import functional_datapipe
  from .minibatch_transformer import MiniBatchTransformer
label_desc: Use image node_id 1 to create a new NegativeSampler object from inherited base classes: MiniBatchTransformer with example: obj = NegativeSampler(datapipe, negative_ratio)