code: def _get_quantiles(
      self,
      tau: Tensor,
      mods: List[Tensor],
      median: Tensor,
  ) -> Tuple[Tensor, Tensor]:
      q_increment = self.q_siren(mods, init=tau)  # type: ignore
      return q_increment, median[:, None] + q_increment
  
imports: import torch
  import numpy
  import torch.nn.functional
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Tuple
  from typing import Union
  from typing import Optional
  from cftool.misc import print_warning
  from cftool.types import tensor_dict_type
  from .base import MLModel
  from .fcnn import FCNN
  from ...types import losses_type
  from ...schema import ILoss
  from ...schema import ITrainer
  from ...schema import TrainerState
  from ...schema import MLEncoderSettings
  from ...schema import MLGlobalEncoderSettings
  from ...constants import LOSS_KEY
  from ...constants import INPUT_KEY
  from ...constants import LABEL_KEY
  from ...constants import PREDICTIONS_KEY
  from ..implicit.siren import make_grid
  from ..implicit.siren import Siren
  from ...misc.toolkit import get_gradient
label_desc: Use image node_id 5 for calling the DDR obj's underlying member method code with example usage: obj._get_quantiles(tau, mods, median) and returns: q_increment,  unknown