class_docstr: Spliced Binned-Pareto univariate distribution.
  
  Arguments
  ----------
      bins_lower_bound: The lower bound of the bin edges
      bins_upper_bound: The upper bound of the bin edges
      numb_bins: The number of equidistance bins to allocate between
          `bins_lower_bound` and `bins_upper_bound`. Default value is 100.
      tail_percentile_gen_pareto: The percentile of the distribution that is
          each tail. Default value is 0.05. NB: This symmetric percentile
          can still represent asymmetric upper and lower tails.
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docstr: Probability for a tensor of data points `x`.
  'x' is to have shape (*batch_shape)
docstr_tok: ["Probability","for","a","tensor","of","data","points","`","x","`",".","'","x","'","is","to","have","shape","(","*","batch_shape",")"]
code: def pdf(self, x):
      """
      Probability for a tensor of data points `x`.
      'x' is to have shape (*batch_shape)
      """
      # By default we put the for training parameter of the pdf on false as
      # one tends to train with the log-prob
      return torch.exp(self.log_prob(x, for_training=False))
  
file_path: gluonts/src/gluonts/torch/distributions/spliced_binned_pareto.py
imports: from typing import Dict, Optional, Tuple, cast
  import torch
  from torch.distributions import constraints
  from gluonts.core.component import validated
  from gluonts.torch.distributions import BinnedUniforms, GeneralizedPareto
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 3 for calling the SplicedBinnedPareto obj's underlying member method code with example usage: obj.pdf(x) and returns: torch