class_docstr: Beta distribution.
  
  Parameters
  ----------
  alpha
      Tensor containing the alpha shape parameters, of shape
      `(*batch_shape, *event_shape)`.
  beta
      Tensor containing the beta shape parameters, of shape
      `(*batch_shape, *event_shape)`.
  F
class_docstr_tok: ["Beta","distribution",".","Parameters","--","--","--","--","--","alpha","Tensor","containing","the","alpha","shape","parameters",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","beta","Tensor","containing","the","beta","shape","parameters",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","F"]
code: def log_prob(self, x: Tensor) -> Tensor:
      F = self.F
      alpha, beta = self.alpha, self.beta
  
      return (
          (alpha - 1) * F.log(x)
          + (beta - 1) * F.log(1 - x)
          - F.gammaln(alpha)
          - F.gammaln(beta)
          + F.gammaln(alpha + beta)
      )
  
imports: from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF, softplus
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 6 for calling the Beta obj's underlying member method code with example usage: obj.log_prob(x) and returns: unknown