class_docstr: This allows us to wrap `EmpiricalDistribution` by any parametric
  distribution and learn the parameters by minimizing CRPS loss on the
  samples of `EmpiricalDistribution`.
  
  See the inference test `test_empirical_distribution` in
  `test.distribution.test_mx_distribution_inference` which checks if the CRPS
  loss is correctly implemented.
class_docstr_tok: ["This","allows","us","to","wrap","`","EmpiricalDistribution","`","by","any","parametric","distribution","and","learn","the","parameters","by","minimizing","CRPS","loss","on","the","samples","of","`","EmpiricalDistribution","`",".","See","the","inference","test","`","test_empirical_distribution","`","in","`","test.distribution.test_mx_distribution_inference","`","which","checks","if","the","CRPS","loss","is","correctly","implemented","."]
code: def event_shape(self) -> Tuple:
      return self.distr_output.event_shape
  
imports: from typing import Optional, Tuple
  import numpy
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, getF
  from .distribution_output import DistributionOutput, ArgProj
label_desc: Use image node_id 5 for calling the EmpiricalDistributionOutput obj's underlying member method code with example usage: obj.event_shape() and returns: self