class_docstr: Create a two parameter Weibull distribution object.
  
  Parameters
  ----------
  concentration : Tensor or scalar
      Concentration/shape parameter of the distribution.
  scale : Tensor or scalar, default 1
      scale parameter of the distribution.
class_docstr_tok: ["Create","a","two","parameter","Weibull","distribution","object",".","Parameters","--","--","--","--","--","concentration",":","Tensor","or","scalar","Concentration\/shape","parameter","of","the","distribution",".","scale",":","Tensor","or","scalar",",","default","1","scale","parameter","of","the","distribution","."]
code: def mean(self):
      return self.scale * np.exp(
          npx.gammaln(1 + 1 / self.concentration)
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/weibull.py
imports: from numpy import euler_gamma
  from .transformed_distribution import TransformedDistribution
  from .exponential import Exponential
  from .constraint import Positive
  from ..transformation import PowerTransform, AffineTransform
  from .utils import sample_n_shape_converter, gammaln
  from ....None import np, npx
label_desc: Use image node_id 4 for calling the Weibull obj's underlying member method code with example usage: obj.mean() and returns: unknown