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.
class_docstr_tok: ["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","."]
code: def __init__(
      self,
      base_distribution: TPPDistribution,
      transforms: List[Bijection],
  ) -> None:
      self.base_distribution = base_distribution
      self.transforms = transforms
      self._check_signs(transforms)
      self.is_reparameterizable = (
          self.base_distribution.is_reparameterizable
      )
  
      # use these to cache shapes and avoid recomputing all steps
      # the reason we cannot do the computations here directly
      # is that this constructor would fail in mx.symbol mode
      self._event_dim: Optional[int] = None
      self._event_shape: Optional[Tuple] = None
      self._batch_shape: Optional[Tuple] = None
  
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 1 to create a new TPPTransformedDistribution object from inherited base classes: TransformedDistribution with example: obj = TPPTransformedDistribution(base_distribution, transforms)