code: def distribution(self, args, loc=None, scale=None) -> Binned:
      probs = args[0]
      bin_centers = args[1]
      F = getF(probs)
  
      bin_centers = F.broadcast_mul(bin_centers, F.ones_like(probs))
      bin_centers = self._scale_bin_centers(
          F, bin_centers, loc=loc, scale=scale
      )
  
      return Binned(
          probs, bin_centers, label_smoothing=self.label_smoothing
      )
  
imports: from typing import List, Optional, Tuple
  import mxnet
  import numpy
  from mxnet import gluon
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import MAX_SUPPORT_VAL, Distribution, _sample_multiple, getF
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 4 for calling the BinnedOutput obj's underlying member method code with example usage: obj.distribution(args, loc, scale) and returns: Binned