class_docstr: TransformedDistribution 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.
  
  Additionally, the sequence of transformations passed to the constructor
  must be increasing.
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docstr: Logarithm of the intensity (a.k.a. hazard) function.
  
  The intensity is defined as :math:`\lambda(y) = p(y) / S(y)`.
docstr_tok: ["Logarithm","of","the","intensity","(","a.k.a",".","hazard",")","function",".","The","intensity","is","defined","as",":","math",":","`","\\lambda","(","y",")","=","p","(","y",")","\/","S","(","y",")","`","."]
code: def log_intensity(self, y: Tensor) -> Tensor:
      r"""
      Logarithm of the intensity (a.k.a. hazard) function.
  
      The intensity is defined as :math:`\lambda(y) = p(y) / S(y)`.
      """
      F = getF(y)
      lp = 0.0
      x = y
      for t in self.transforms[::-1]:
          x = t.f_inv(y)
          ladj = t.log_abs_det_jac(x, y)
          lp -= sum_trailing_axes(F, ladj, self.event_dim - t.event_dim)
          y = x
      return self.base_distribution.log_intensity(x) + lp
  
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 TPPTransformedDistribution obj's underlying member method code with example usage: obj.log_intensity(y) and returns: unknown