class_docstr: Negative binomial distribution with `total_count` and `probs` or `logits` parameters.
  
  Based on torch.distributions.NegativeBinomial, with added `cdf` and `icdf` methods.
class_docstr_tok: ["Negative","binomial","distribution","with","`","total_count","`","and","`","probs","`","or","`","logits","`","parameters",".","Based","on","torch.distributions.NegativeBinomial",",","with","added","`","cdf","`","and","`","icdf","`","methods","."]
code: def __init__(
      self,
      total_count: Union[float, torch.Tensor],
      probs: Optional[Union[float, torch.Tensor]] = None,
      logits: Optional[Union[float, torch.Tensor]] = None,
      validate_args=None,
  ):
      super().__init__(
          total_count=total_count,
          probs=probs,
          logits=logits,
          validate_args=validate_args,
      )
  
file_path: gluonts/src/gluonts/torch/distributions/negative_binomial.py
imports: from typing import Dict, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch.distributions import Distribution
  from torch.distributions import NegativeBinomial
  from gluonts.util import lazy_property
  from scipy.stats import nbinom
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 1 to create a new NegativeBinomial object from inherited base classes: TorchNegativeBinomial with example: obj = NegativeBinomial(total_count, probs, logits, validate_args)