class_docstr: A class representing empirical distribution.
  
  The target can be vector/tensor-valued, i.e., `event_shape` can be larger
  than or equal to 1. However, note that each dimension is assumed to be
  independent when computing variance and CRPS loss.
  
  Also, for computing CDF and quantiles, it is assumede that samples are
  distinct along the samples dimension, which should almost always be the
  case if samples are drawn from continuous distributions.
  
  Parameters
  ----------
  samples
      Tensor containing samples, of shape
      `(num_samples, *batch_shape, *event_shape)`.
  event_dim
      Number of event dimensions, i.e., length of the `event_shape` tuple.
      This is `0` for distributions over scalars, `1` over vectors,
      `2` over matrices, and so on.
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code: def loss(self, x: Tensor) -> Tensor:
      return self.crps_univariate(x=x)
  
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 13 for calling the EmpiricalDistribution obj's underlying member method code with example usage: obj.loss(x) and returns: self