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Given inputs tensors, stochastically resamples each at a given rate.

For example, if the inputs are [[a1, a2], [b1, b2]] and the rates tensor contains [3, 1], then the return value may look like [[a1, a2, a1, a1], [b1, b2, b1, b1]]. However, many other outputs are possible, since this is stochastic -- averaged over many repeated calls, each set of inputs should appear in the output rate times the number of invocations.


  • inputs: A list of tensors, each of which has a shape of [batch_size, ...]
  • rates: A tensor of shape [batch_size] containing the resampling rates for each input.
  • scope: Scope for the op.
  • seed: Random seed to use.
  • back_prop: Whether to allow back-propagation through this op.


Selections from the input tensors.