class_docstr: Create a Gamma distribution object.
  
  Parameters
  ----------
  shape : Tensor or scalar
      shape parameter of the distribution, often represented by `k` or `\alpha`
  scale : Tensor or scalar, default 1
      scale parameter of the distribution, often represented by `\theta`,
      `\theta` = 1 / `\beta`, where `\beta` stands for the rate parameter.
class_docstr_tok: ["Create","a","Gamma","distribution","object",".","Parameters","--","--","--","--","--","shape",":","Tensor","or","scalar","shape","parameter","of","the","distribution",",","often","represented","by","`","k","`","or","`","\\alpha","`","scale",":","Tensor","or","scalar",",","default","1","scale","parameter","of","the","distribution",",","often","represented","by","`","\\theta","`",",","`","\\theta","`","=","1","\/","`","\\beta","`",",","where","`","\\beta","`","stands","for","the","rate","parameter","."]
code: def sample_n(self, size=None):
      return (
          np.random.gamma(self.shape, 1, sample_n_shape_converter(size))
          * self.scale
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/gamma.py
imports: from .exp_family import ExponentialFamily
  from .constraint import Real, Positive
  from .utils import sample_n_shape_converter, gammaln, digamma
  from ....None import np
label_desc: Use image node_id 5 for calling the Gamma obj's underlying member method code with example usage: obj.sample_n(size) and returns: unknown