class_docstr: Binned uniforms distribution.
  
  Args:
      bins_lower_bound (float): The lower bound of the bin edges
      bins_upper_bound (float): The upper bound of the bin edges
      numb_bins (int): The number of equidistance bins to allocate between
          `bins_lower_bound` and `bins_upper_bound`. Default value is 100.
      logits (tensor): the logits defining the probability of each bins.
          These are softmaxed. The tensor is of shape (*batch_shape,)
      validate_args (bool) from the pytorch Distribution class
class_docstr_tok: ["Binned","uniforms","distribution",".","Args",":","bins_lower_bound","(","float",")",":","The","lower","bound","of","the","bin","edges","bins_upper_bound","(","float",")",":","The","upper","bound","of","the","bin","edges","numb_bins","(","int",")",":","The","number","of","equidistance","bins","to","allocate","between","`","bins_lower_bound","`","and","`","bins_upper_bound","`",".","Default","value","is","100.","logits","(","tensor",")",":","the","logits","defining","the","probability","of","each","bins",".","These","are","softmaxed",".","The","tensor","is","of","shape","(","*","batch_shape",",",")","validate_args","(","bool",")","from","the","pytorch","Distribution","class"]
code: def variance(self):
      raise NotImplementedError
  
imports: from typing import Dict, Optional, Tuple, cast
  import torch
  import torch.nn.functional
  from torch.distributions import Distribution, constraints
  from gluonts.core.component import validated
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 18 for calling the BinnedUniforms obj's underlying member method code with example usage: obj.variance() without return types