<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
 """Draw random samples from a gamma distribution. Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale). Parameters alpha : float or NDArray, optional The shape of the gamma distribution. Should be greater than zero. beta : float or NDArray, optional The scale of the gamma distribution. Should be greater than zero. Default is equal to 1. shape : int or tuple of ints, optional The number of samples to draw. If shape is, e.g., `(m, n)` and `alpha` and `beta` are scalars, output shape will be `(m, n)`. If `alpha` and `beta` 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 `[alpha, beta)` 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 `alpha.context` when `alpha` is an NDArray. out : NDArray, optional Store output to an existing NDArray. Returns ------- NDArray If input `shape` has shape, e.g., `(m, n)` and `alpha` and `beta` are scalars, output shape will be `(m, n)`. If `alpha` and `beta` 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 `[alpha, beta)` pair. Examples -------- [ 1.93308783] <NDArray 1 @cpu(0)> [ 0.48216391 2.09890771] <NDArray 2 @cpu(0)> [[ 3.24343276 0.94137681] [ 3.52734375 0.45568955] [ 14.26264095 14.0170126 ]] <NDArray 3x2 @cpu(0)> """

    return _random_helper(_internal._random_gamma, _internal._sample_gamma,
                          [alpha, beta], shape, dtype, ctx, out, kwargs)