class_docstr: Create a Normal distribution object.
  
  Parameters
  ----------
  loc : Tensor or scalar, default 0
      mean of the distribution.
  scale : Tensor or scalar, default 1
      standard deviation of the distribution
class_docstr_tok: ["Create","a","Normal","distribution","object",".","Parameters","--","--","--","--","--","loc",":","Tensor","or","scalar",",","default","0","mean","of","the","distribution",".","scale",":","Tensor","or","scalar",",","default","1","standard","deviation","of","the","distribution"]
code: def __init__(self, loc=0.0, scale=1.0, validate_args=None):
      self.loc = loc
      self.scale = scale
      super(Normal, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/normal.py
imports: import math
  from .constraint import Real, Positive
  from .exp_family import ExponentialFamily
  from .utils import erf, erfinv
  from ....None import np, npx
label_desc: Use image node_id 1 to create a new Normal object from inherited base classes: ExponentialFamily with example: obj = Normal(loc, scale, validate_args)