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 __init__(self, concentration, scale=1.0, validate_args=None):
      self.concentration = concentration
      self.scale = scale
      base_dist = Exponential()
      super(Weibull, self).__init__(
          base_dist,
          [
              PowerTransform(1 / self.concentration),
              AffineTransform(0, self.scale),
          ],
      )
  
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 1 to create a new Weibull object from inherited base classes: TransformedDistribution with example: obj = Weibull(concentration, scale, validate_args)