class_docstr: A mixture distribution where each component is a Distribution.
  
  Parameters
  ----------
  mixture_probs
      A tensor of mixing probabilities. The entries should all be positive
      and sum to 1 across the last dimension. Shape: (..., k), where k is
      the number of distributions to be mixed. All axes except the last one
      should either coincide with the ones from the component distributions,
      or be 1 (in which case, the mixing coefficient is shared across
      the axis).
  components
      A list of k Distribution objects representing the mixture components.
      Distributions can be of different types. Each component's support
      should be made of tensors of shape (..., d).
  F
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet
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code: def cdf(self, x: Tensor) -> Tensor:
      F = self.F
      cdf_values = F.stack(
          *[c.cdf(x) for c in self.components], axis=-1
      )
      erg = F.sum(
          F.broadcast_mul(cdf_values, self.mixture_probs, axis=-1),
          axis=-1,
      )
      return erg
  
imports: from typing import List, Optional, Tuple
  import mxnet
  import numpy
  from mxnet import gluon
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import MAX_SUPPORT_VAL, Distribution, _expand_param, _index_tensor, getF
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 10 for calling the MixtureDistribution obj's underlying member method code with example usage: obj.cdf(x) and returns: erg