class_docstr: A binned distribution defined by a set of bins via
  bin centers and bin probabilities.
  
  Parameters
  ----------
  bin_log_probs
      Tensor containing log probabilities of the bins, of shape
      `(*batch_shape, num_bins)`.
  bin_centers
      Tensor containing the bin centers, of shape `(*batch_shape, num_bins)`.
  F
  label_smoothing
      The label smoothing weight, real number in `[0, 1)`. Default `None`. If
      not `None`, then the loss of the distribution will be "label smoothed"
      cross-entropy. For example, instead of computing cross-entropy loss
      between the estimated bin probabilities and a hard-label
      (one-hot encoding) `[1, 0, 0]`, a soft label of `[0.9, 0.05, 0.05]` is
      taken as the ground truth (when `label_smoothing=0.15`). See(Muller et
      al., 2019) [MKH19]_, for further reference.
class_docstr_tok: ["A","binned","distribution","defined","by","a","set","of","bins","via","bin","centers","and","bin","probabilities",".","Parameters","--","--","--","--","--","bin_log_probs","Tensor","containing","log","probabilities","of","the","bins",",","of","shape","`","(","*","batch_shape",",","num_bins",")","`",".","bin_centers","Tensor","containing","the","bin","centers",",","of","shape","`","(","*","batch_shape",",","num_bins",")","`",".","F","label_smoothing","The","label","smoothing","weight",",","real","number","in","`","[","0",",","1",")","`",".","Default","`","None","`",".","If","not","`","None","`",",","then","the","loss","of","the","distribution","will","be","``","label","smoothed","''","cross-entropy",".","For","example",",","instead","of","computing","cross-entropy","loss","between","the","estimated","bin","probabilities","and","a","hard-label","(","one-hot","encoding",")","`","[","1",",","0",",","0","]","`",",","a","soft","label","of","`","[","0.9",",","0.05",",","0.05","]","`","is","taken","as","the","ground","truth","(","when","`","label_smoothing=0.15","`",")",".","See","(","Muller","et","al.",",","2019",")","[","MKH19","]","_",",","for","further","reference","."]
docstr: Computes the edges of the bins based on the centers. The first and last
  edge are set to :math:`10^{-10}` and
  :math:`10^{10}`, repsectively.
  
  Parameters
  ----------
  F
  bin_centers
      Tensor of shape `(*batch_shape, num_bins)`.
  
  Returns
  -------
  Tensor
      Tensor of shape (*batch.shape, num_bins+1)
docstr_tok: ["Computes","the","edges","of","the","bins","based","on","the","centers",".","The","first","and","last","edge","are","set","to",":","math",":","`","10^","{","-10","}","`","and",":","math",":","`","10^","{","10","}","`",",","repsectively",".","Parameters","--","--","--","--","--","F","bin_centers","Tensor","of","shape","`","(","*","batch_shape",",","num_bins",")","`",".","Returns","--","--","--","-","Tensor","Tensor","of","shape","(","*","batch.shape",",","num_bins+1",")"]
code: def _compute_edges(F, bin_centers: Tensor) -> Tensor:
      r"""
      Computes the edges of the bins based on the centers. The first and last
      edge are set to :math:`10^{-10}` and
      :math:`10^{10}`, repsectively.
  
      Parameters
      ----------
      F
      bin_centers
          Tensor of shape `(*batch_shape, num_bins)`.
  
      Returns
      -------
      Tensor
          Tensor of shape (*batch.shape, num_bins+1)
      """
  
      low = (
          F.zeros_like(bin_centers.slice_axis(axis=-1, begin=0, end=1))
          - 1.0e10
      )
      high = (
          F.zeros_like(bin_centers.slice_axis(axis=-1, begin=0, end=1))
          + 1.0e10
      )
  
      means = (
          F.broadcast_add(
              bin_centers.slice_axis(axis=-1, begin=1, end=None),
              bin_centers.slice_axis(axis=-1, begin=0, end=-1),
          )
          / 2.0
      )
  
      return F.concat(low, means, high, dim=-1)
  
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 Binned obj's underlying member method code with example usage: obj._compute_edges(F, bin_centers) and returns: F