code: def randn(*shape, **kwargs):
      """Draw random samples from a normal (Gaussian) distribution.
  
      Samples are distributed according to a normal distribution parametrized
      by *loc* (mean) and *scale* (standard deviation).
  
  
      Parameters
      ----------
      loc : float or Symbol, optional
          Mean (centre) of the distribution.
      scale : float or Symbol, optional
          Standard deviation (spread or width) of the distribution.
      shape : int or tuple of ints
          The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and
          `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`
          are NDArrays with shape, e.g., `(x, y)`, then output will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'
      """
      loc = kwargs.pop("loc", 0)
      scale = kwargs.pop("scale", 1)
      dtype = kwargs.pop("dtype", _Null)
      assert isinstance(loc, (int, float, Symbol))
      assert isinstance(scale, (int, float, Symbol))
      return _random_helper(
          _internal._random_normal,
          _internal._sample_normal,
          [loc, scale],
          shape,
          dtype,
          kwargs,
      )
  
file_path: mxnet/python/mxnet/symbol/random.py
imports: from ..base import numeric_types, _Null
  from .None import _internal
  from .symbol import Symbol
label_desc: Use image node_id 4 for calling a global function with example usage: randn() and returns: _random_helper