code: def __init__(self, quantiles: List[float]) -> None:
      assert len(quantiles) > 0
      assert all(0.0 < q < 1.0 for q in quantiles)
      self._quantiles = sorted(quantiles)
      self.num_quantiles = len(self._quantiles)
      self.args_dim = {"outputs": self.num_quantiles}
  
file_path: gluonts/src/gluonts/torch/distributions/quantile_output.py
imports: from typing import List, Optional, Tuple
  import torch
  from gluonts.core.component import validated
  from gluonts.model.forecast_generator import ForecastGenerator, QuantileForecastGenerator
  from .distribution_output import Output
label_desc: Use image node_id 1 to create a new QuantileOutput object from inherited base classes: Output with example: obj = QuantileOutput(quantiles)