parameters: (scale=1, shape=_Null, dtype=_Null, **kwargs)
return_statement: return _random_helper(_internal._random_exponential, _internal._sample_exponential,
                            [1.0/scale], shape, dtype, kwargs)
docstring: 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 Symbol, 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 Symbol 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'
  
      Returns
      -------
      Symbol
          If input `shape` has dimensions, e.g., `(m, n)`, and `scale` is
          a scalar, returned Symbol will have shape `(m, n)`. If `scale`
          is a Symbol with shape, e.g., `(x, y)`, returned Symbol will resolve to
          shape `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `scale`.
docstring_summary: r"""Draw samples from an exponential distribution.
docstring_tokens: r Draw samples from an exponential distribution .
function: def exponential(scale=1, shape=_Null, dtype=_Null, **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 Symbol, 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 Symbol 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'
  
      Returns
      -------
      Symbol
          If input `shape` has dimensions, e.g., `(m, n)`, and `scale` is
          a scalar, returned Symbol will have shape `(m, n)`. If `scale`
          is a Symbol with shape, e.g., `(x, y)`, returned Symbol will resolve to
          shape `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `scale`.
      """
      return _random_helper(_internal._random_exponential, _internal._sample_exponential,
                            [1.0/scale], shape, dtype, kwargs)
function_tokens: def exponential ( scale = 1 , shape = _Null , dtype = _Null , * * kwargs ) : return _random_helper ( _internal . _random_exponential , _internal . _sample_exponential , [ 1.0 / scale ] , shape , dtype , kwargs )