code: def exponential(
      scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs
  ):
      r"""Draw samples from an exponential distribution.
  
      Its probability density function is
  
      .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
  
      for x > 0 and 0 elsewhere. \beta is the scale parameter, which is the
      inverse of the rate parameter \lambda = 1/\beta.
  
      Parameters
      ----------
      scale : float or NDArray, optional
          The scale parameter, \beta = 1/\lambda.
      shape : int or tuple of ints, optional
          The number of samples to draw. If shape is, e.g., `(m, n)` and `scale` is
          a scalar, output shape will be `(m, n)`. If `scale`
          is an NDArray with shape, e.g., `(x, y)`, then output will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `scale`.
      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
          `scale.context` when `scale` is an NDArray.
      out : NDArray, optional
          Store output to an existing NDArray.
  
      Returns
      -------
      NDArray
          If input `shape` has shape, e.g., `(m, n)` and `scale` is a scalar, output shape will
          be `(m, n)`. If `scale` is an NDArray with shape, e.g., `(x, y)`, then `output`
          will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each entry in scale.
  
      Examples
      --------
      >>> mx.nd.random.exponential(1)
      [ 0.79587454]
      <NDArray 1 @cpu(0)>
      >>> mx.nd.random.exponential(1, shape=(2,))
      [ 0.89856035  1.25593066]
      <NDArray 2 @cpu(0)>
      >>> scale = mx.nd.array([1,2,3])
      >>> mx.nd.random.exponential(scale, shape=2)
      [[  0.41063145   0.42140478]
       [  2.59407091  10.12439728]
       [  2.42544937   1.14260709]]
      <NDArray 3x2 @cpu(0)>
      """
      return _random_helper(
          _internal._random_exponential,
          _internal._sample_exponential,
          [1.0 / 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 6 for calling a global function with example usage: exponential(scale, shape, dtype, ctx, out) and returns: _random_helper