class_docstr: Creates a generalised normal (also called Gaussian) distribution parameterized by
  :attr:`loc` and :attr:`scale` and :attr:`beta`.
  Example::
      >>> m = GeneralisedNormal(torch.tensor([0.0]), torch.tensor([1.0]), 2)
  Args:
      loc (float or Tensor): mean of the distribution (often referred to as mu)
      scale (float or Tensor):
      beta (float or Tensor):
code: def _log_normalizer(self, x, y):
      raise NotImplementedError
  
imports: import math
  from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.exp_family import ExponentialFamily
  from torch.distributions.utils import _standard_normal, broadcast_all
  from scipy.stats import gennorm
  import numpy
label_desc: Use image node_id 13 for calling the GeneralisedNormal obj's underlying member method code with example usage: obj._log_normalizer(x, y) without return types