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","."]
docstr: Make sure that the transformations are all increasing.
  
  This condition significantly simplifies the log_survival and
  sample_conditional functions.
docstr_tok: ["Make","sure","that","the","transformations","are","all","increasing",".","This","condition","significantly","simplifies","the","log_survival","and","sample_conditional","functions","."]
code: def _check_signs(self, transforms):
      """
      Make sure that the transformations are all increasing.
  
      This condition significantly simplifies the log_survival and
      sample_conditional functions.
      """
      sign = 1.0
      for t in transforms:
          sign = sign * t.sign
      if (sign != 1.0).asnumpy().any():
          raise ValueError("The transformations must be increasing.")
  
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 TPPTransformedDistribution obj's underlying member method code with example usage: obj._check_signs(transforms) without return types