class_docstr: Creates a Exponential distribution parameterized by :attr:`rate`.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = Exponential(torch.tensor([1.0]))
      >>> m.sample()  # Exponential distributed with rate=1
      tensor([ 0.1046])
  
  Args:
      rate (float or Tensor): rate = 1 / scale of the distribution
class_docstr_tok: ["Creates","a","Exponential","distribution","parameterized","by",":","attr",":","`","rate","`",".","Example",":",":",">",">",">","#","xdoctest",":","+IGNORE_WANT","(","``","non-deterministic","''",")",">",">",">","m","=","Exponential","(","torch.tensor","(","[","1.0","]",")",")",">",">",">","m.sample","(",")","#","Exponential","distributed","with","rate=1","tensor","(","[","0.1046","]",")","Args",":","rate","(","float","or","Tensor",")",":","rate","=","1","\/","scale","of","the","distribution"]
code: def log_prob(self, value):
      if self._validate_args:
          self._validate_sample(value)
      return self.rate.log() - self.rate * value
  
file_path: pytorch/torch/distributions/exponential.py
imports: from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.exp_family import ExponentialFamily
  from torch.distributions.utils import broadcast_all
label_desc: Use image node_id 8 for calling the Exponential obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown