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","."]
code: def log_prob(self, x: Tensor) -> Tensor:
      return self.log_intensity(x) + self.log_survival(x)
  
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 3 for calling the TPPDistribution obj's underlying member method code with example usage: obj.log_prob(x) and returns: unknown