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"]
docstr: Generate samples of `size` from the normal distribution
  parameterized by `self._loc` and `self._scale`
  
  Parameters
  ----------
  size : Tuple, Scalar, or None
      Size of samples to be generated. If size=None, the output shape
      will be `broadcast(loc, scale).shape`
  
  Returns
  -------
  Tensor
      Samples from Normal distribution.
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code: def sample(self, size=None):
      r"""Generate samples of `size` from the normal distribution
      parameterized by `self._loc` and `self._scale`
  
      Parameters
      ----------
      size : Tuple, Scalar, or None
          Size of samples to be generated. If size=None, the output shape
          will be `broadcast(loc, scale).shape`
  
      Returns
      -------
      Tensor
          Samples from Normal distribution.
      """
      return np.random.normal(self.loc, self.scale, size)
  
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 3 for calling the Normal obj's underlying member method code with example usage: obj.sample(size) and returns: np