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 cdf(self, value: torch.Tensor) -> torch.Tensor:
      if self._validate_args:
          self._validate_sample(value)
      result = self.scipy_nbinom.cdf(value.detach().cpu().numpy())
      return torch.tensor(
          result, device=value.device, dtype=value.dtype
      )
  
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 2 for calling the NegativeBinomial obj's underlying member method code with example usage: obj.cdf(value) and returns: torch