expand(batch_shape, _instance=None)
log_prob(value)
property logits
property mean
property mode
property param_shape
property probs
sample(sample_shape=torch.Size([]))
support = IntegerGreaterThan(lower_bound=0)
property variance
Normal
class torch.distributions.normal.Normal(loc, scale, validate_args=None)
Bases: "ExponentialFamily"
Creates a normal (also called Gaussian) distribution parameterized
   by "loc" and "scale".
Example:
  >>> m = Normal(torch.tensor([0.0]), torch.tensor([1.0]))
  >>> m.sample()  # normally distributed with loc=0 and scale=1
  tensor([ 0.1046])

Parameters:
      * loc (float or Tensor) -- mean of the distribution
        (often referred to as mu)
  * **scale** (*float** or **Tensor*) -- standard deviation of the
    distribution (often referred to as sigma)

arg_constraints = {'loc': Real(), 'scale': GreaterThan(lower_bound=0.0)}
cdf(value)
entropy()