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.
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code: def event_dim(self) -> int:
      return 0
  
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 8 for calling the Binned obj's underlying member method code with example usage: obj.event_dim() and returns: int