code: 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
      --------
      >>> mx.nd.random.gamma(1, 1)
      [ 1.93308783]
      <NDArray 1 @cpu(0)>
      >>> mx.nd.random.gamma(1, 1, shape=(2,))
      [ 0.48216391  2.09890771]
      <NDArray 2 @cpu(0)>
      >>> alpha = mx.nd.array([1,2,3])
      >>> beta = mx.nd.array([2,3,4])
      >>> mx.nd.random.gamma(alpha, beta, shape=2)
      [[  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,
      )
  
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 7 for calling a global function with example usage: gamma(alpha, beta, shape, dtype, ctx, out) and returns: _random_helper