class_docstr: Adapter for DPRNN family.
  RNN returns a tuple, but our testing tools need the model to return a single tensor in output.
  We do this adaption here.
class_docstr_tok: ["Adapter","for","DPRNN","family",".","RNN","returns","a","tuple",",","but","our","testing","tools","need","the","model","to","return","a","single","tensor","in","output",".","We","do","this","adaption","here","."]
code: def __init__(self, dp_rnn):
      super().__init__()
      self.dp_rnn = dp_rnn
  
file_path: opacus/opacus/tests/grad_samples/dp_rnn_test.py
imports: import hypothesis.strategies
  import torch
  import torch.nn
  from hypothesis import given, settings
  from opacus.layers import DPGRU, DPLSTM, DPRNN
  from opacus.utils.packed_sequences import _gen_packed_data
  from .common import GradSampleHooks_test
label_desc: Use image node_id 1 to create a new DPRNNAdapter object from inherited base classes: nn with example: obj = DPRNNAdapter(dp_rnn)