code: def __init__(self, q: Union[float, List, Tuple, np.ndarray]):
      super().__init__()
      if isinstance(q, float):
          self.register_buffer("q", torch.tensor([q], torch.float32))
      else:
          q = np.asarray(q, np.float32).reshape([1, -1])
          self.register_buffer("q", to_torch(q))
  
imports: import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Any
  from typing import List
  from typing import Tuple
  from typing import Union
  from typing import Optional
  from cftool.misc import shallow_copy_dict
  from cftool.array import iou
  from cftool.array import corr
  from cftool.array import to_torch
  from cftool.types import tensor_dict_type
  from ..types import losses_type
  from ..schema import ILoss
  from ..schema import TrainerState
  from ..constants import LOSS_KEY
  from ..constants import INPUT_KEY
  from ..constants import LABEL_KEY
  from ..constants import PREDICTIONS_KEY
label_desc: Use image node_id 1 to create a new QuantileLoss object from inherited base classes: ILoss with example: obj = QuantileLoss(q)