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 broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance.loc = np.broadcast_to(self.loc, batch_shape)
      new_instance.scale = np.broadcast_to(self.scale, batch_shape)
      super(Normal, new_instance).__init__(
          event_dim=self.event_dim, validate_args=False
      )
      new_instance._validate_args = self._validate_args
      return new_instance
  
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 5 for calling the Normal obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance