class_docstr: Weibull distribution.
  
  We use the parametrization of the Weibull distribution using the rate
  parameter :math:`b > 0` and the shape parameter :math:`k > 0`. The PDF is
  :math:`p(x) = b * k * x^{(k - 1)} * \exp(-b * x^k)`. An alternative
  parametrization is often used (e.g. on Wikipedia), where we use the scale
  parameter :math:`\lambda > 0` and the shape parameter :math:`k > 0`, and
  :math:`\lambda = b^{-1/k}`.
docstr: Logarithm of the intensity (a.k.a. hazard) function.
  
  The intensity is defined as :math:`\lambda(x) = p(x) / S(x)`.
  
  The intensity of the Weibull distribution is
  :math:`\lambda(x) = b * k * x^{k - 1}`.
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code: def log_intensity(self, x: Tensor) -> Tensor:
      r"""
      Logarithm of the intensity (a.k.a. hazard) function.
  
      The intensity is defined as :math:`\lambda(x) = p(x) / S(x)`.
  
      The intensity of the Weibull distribution is
      :math:`\lambda(x) = b * k * x^{k - 1}`.
      """
      log_x = x.clip(1e-10, np.inf).log()
      return (
          self.rate.log() + self.shape.log() + (self.shape - 1) * log_x
      )
  
imports: from typing import Dict, Optional, Tuple
  import numpy
  from mxnet import autograd, nd
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.distribution import getF
  from .base import TPPDistribution, TPPDistributionOutput
label_desc: Use image node_id 6 for calling the Weibull obj's underlying member method code with example usage: obj.log_intensity(x) and returns: unknown