class_docstr: Negative binomial distribution, i.e. the distribution of the number of
  successes in a sequence of independent Bernoulli trials.
  
  Parameters
  ----------
  mu
      Tensor containing the means, of shape `(*batch_shape, *event_shape)`.
  alpha
      Tensor of the shape parameters, of shape
      `(*batch_shape, *event_shape)`.
  F
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code: def __init__(self, mu: Tensor, alpha: Tensor) -> None:
      self.mu = mu
      self.alpha = alpha
  
imports: from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .deterministic import DeterministicOutput
  from .distribution import Distribution, _sample_multiple, getF, softplus
  from .distribution_output import DistributionOutput
  from .mixture import MixtureDistributionOutput
label_desc: Use image node_id 1 to create a new NegativeBinomial object from inherited base classes: Distribution with example: obj = NegativeBinomial(mu, alpha)