code: def normal(
      loc=0,
      scale=1,
      shape=_Null,
      dtype=_Null,
      ctx=None,
      out=None,
      **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 NDArray, optional
          Mean (centre) of the distribution.
      scale : float or NDArray, optional
          Standard deviation (spread or width) of the distribution.
      shape : int or tuple of ints, optional
          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'
      ctx : Context, optional
          Device context of output. Default is current context. Overridden by
          `loc.context` when `loc` is an NDArray.
      out : NDArray, optional
          Store output to an existing NDArray.
  
      Returns
      -------
      NDArray
          An NDArray of type `dtype`. If input `shape` has shape, 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.
  
      Examples
      --------
      >>> mx.nd.random.normal(0, 1)
      [ 2.21220636]
      <NDArray 1 @cpu(0)>
      >>> mx.nd.random.normal(0, 1, ctx=mx.gpu(0))
      [ 0.29253659]
      <NDArray 1 @gpu(0)>
      >>> mx.nd.random.normal(-1, 1, shape=(2,))
      [-0.2259962  -0.51619542]
      <NDArray 2 @cpu(0)>
      >>> loc = mx.nd.array([1,2,3])
      >>> scale = mx.nd.array([2,3,4])
      >>> mx.nd.random.normal(loc, scale, shape=2)
      [[ 0.55912292  3.19566321]
       [ 1.91728961  2.47706747]
       [ 2.79666662  5.44254589]]
      <NDArray 3x2 @cpu(0)>
      """
      return _random_helper(
          _internal._random_normal,
          _internal._sample_normal,
          [loc, scale],
          shape,
          dtype,
          ctx,
          out,
          kwargs,
      )
  
file_path: mxnet/python/mxnet/ndarray/random.py
imports: from ..base import numeric_types, _Null
  from ..context import current_context
  from .None import _internal
  from .ndarray import NDArray
label_desc: Use image node_id 3 for calling a global function with example usage: normal(loc, scale, shape, dtype, ctx, out) and returns: _random_helper