class_docstr: Distribution used in temporal point processes.
  
  This class must implement new methods log_intensity, log_survival that are
  necessary for computing log-likelihood of TPP realizations. Also,
  sample_conditional is necessary for sampling TPPs.
class_docstr_tok: ["Distribution","used","in","temporal","point","processes",".","This","class","must","implement","new","methods","log_intensity",",","log_survival","that","are","necessary","for","computing","log-likelihood","of","TPP","realizations",".","Also",",","sample_conditional","is","necessary","for","sampling","TPPs","."]
docstr: Logarithm of the survival function `\log S(x) = \log(1 - CDF(x))`.
docstr_tok: ["Logarithm","of","the","survival","function","`","\\log","S","(","x",")","=","\\log","(","1","-","CDF","(","x",")",")","`","."]
code: def log_survival(self, x: Tensor) -> Tensor:
      r"""
      Logarithm of the survival function `\log S(x) = \log(1 - CDF(x))`.
      """
      raise NotImplementedError()
  
imports: from typing import List, Optional, Tuple, Union
  import numpy
  from mxnet import autograd
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.bijection import AffineTransformation, Bijection
  from gluonts.mx.distribution.distribution import Distribution, getF
  from gluonts.mx.distribution.distribution_output import DistributionOutput
  from gluonts.mx.distribution.transformed_distribution import TransformedDistribution, sum_trailing_axes
label_desc: Use image node_id 2 for calling the TPPDistribution obj's underlying member method code with example usage: obj.log_survival(x) without return types