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
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docstr: Generate samples of (batch_size + broadcast(loc, scale).shape)
  from the normal distribution parameterized by `self._loc` and `self._scale`
  
  Parameters
  ----------
  size : Tuple, Scalar, or None
      Size of independent batch to be generated from the distribution.
  
  Returns
  -------
  Tensor
      Samples from Normal distribution.
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code: def sample_n(self, size=None):
      r"""Generate samples of (batch_size + broadcast(loc, scale).shape)
      from the normal distribution parameterized by `self._loc` and `self._scale`
  
      Parameters
      ----------
      size : Tuple, Scalar, or None
          Size of independent batch to be generated from the distribution.
  
      Returns
      -------
      Tensor
          Samples from Normal distribution.
      """
      return npx.random.normal_n(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 4 for calling the Normal obj's underlying member method code with example usage: obj.sample_n(size) and returns: npx