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
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docstr: Inverse cdf of a tensor of quantile `quantiles`
  'quantiles' is of shape (*batch_shape) with values between (0.0, 1.0)
  
  This is the function to be called from the outside.
docstr_tok: ["Inverse","cdf","of","a","tensor","of","quantile","`","quantiles","`","'quantiles","'","is","of","shape","(","*","batch_shape",")","with","values","between","(","0.0",",","1.0",")","This","is","the","function","to","be","called","from","the","outside","."]
code: def icdf(self, quantiles):
      """
      Inverse cdf of a tensor of quantile `quantiles`
      'quantiles' is of shape (*batch_shape) with values between (0.0, 1.0)
  
      This is the function to be called from the outside.
      """
      assert (
          quantiles >= 0.0
      ).all(), "quantiles must be between (0.0, 1.0)"
      assert (
          quantiles <= 1.0
      ).all(), "quantiles must be between (0.0, 1.0)"
  
      # If given a single value as quantile, we put it to batch size
      if (
          len(quantiles.shape) == 0
          or len(quantiles.shape) == 1
          and quantiles.shape[0] == 1
      ):
          batch_shape = self.bins_prob[..., 0].shape
          quantiles = quantiles.repeat(batch_shape)
  
      for i in range(0, len(quantiles.shape)):
          assert quantiles.shape[i] == self.batch_shape[i], (
              "We expect the quantile to be either a single float or a "
              "tensor of size batch_shape"
          )
  
      return self._inverse_cdf(quantiles)
  
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 11 for calling the BinnedUniforms obj's underlying member method code with example usage: obj.icdf(quantiles) and returns: self