code: def get_percentile_min_max(
      input, lower_percentile, upper_percentile, output_tensor=False
  ):
      """
      Calculate the percentile max and min values in a given tensor
  
      Args:
          input (`torch.Tensor`):
              The target tensor to calculate percentile max and min.
          lower_percentile (`float`):
              If 0.1, means we return the value of the smallest 0.1% value in the tensor as percentile min.
          upper_percentile (`float`):
              If 99.9, means we return the value of the largest 0.1% value in the tensor as percentile max.
          output_tensor (`bool`, *optional*, defaults to `False`):
              If True, this function returns tensors, otherwise it returns values.
  
      Returns:
          `Tuple(torch.Tensor, torch.Tensor)`: Percentile min and max value of *input*
      """
      input_length = input.shape[0]
  
      lower_index = round(input_length * (1 - lower_percentile * 0.01))
      upper_index = round(input_length * upper_percentile * 0.01)
  
      upper_bound = torch.kthvalue(input, k=upper_index).values
  
      if lower_percentile == 0:
          lower_bound = upper_bound * 0
          # lower_index += 1
      else:
          lower_bound = -torch.kthvalue(-input, k=lower_index).values
  
      if not output_tensor:
          lower_bound = lower_bound.item()
          upper_bound = upper_bound.item()
      return lower_bound, upper_bound
  
imports: import decimal
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Function
  from ...utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_percentile_min_max(input, lower_percentile, upper_percentile, output_tensor) and returns: lower_bound,  upper_bound