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
  nbins: The number of equidistance bins to allocate between `bins_lower_bound` and `bins_upper_bound`. Default value is 100.
  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.
docstr: Moves members to a specified torch.device.
docstr_tok: ["Moves","members","to","a","specified","torch.device","."]
code: def to_device(self, device):
      """
      Moves members to a specified torch.device.
      """
      self.device = device
      self.bin_min = self.bin_min.to(device)
      self.bin_max = self.bin_max.to(device)
      self.bin_edges = self.bin_edges.to(device)
      self.bin_widths = self.bin_widths.to(device)
      self.bin_centres = self.bin_centres.to(device)
      self.logits = self.logits.to(device)
  
imports: from typing import Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from .genpareto import GenPareto
label_desc: Use image node_id 2 for calling the SplicedBinnedPareto obj's underlying member method code with example usage: obj.to_device(device) without return types