class_docstr: Creates a Gamma distribution parameterized by shape :attr:`concentration` and :attr:`rate`.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0]))
      >>> m.sample()  # Gamma distributed with concentration=1 and rate=1
      tensor([ 0.1046])
  
  Args:
      concentration (float or Tensor): shape parameter of the distribution
          (often referred to as alpha)
      rate (float or Tensor): rate = 1 / scale of the distribution
          (often referred to as beta)
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code: def cdf(self, value):
      if self._validate_args:
          self._validate_sample(value)
      return torch.special.gammainc(
          self.concentration, self.rate * value
      )
  
file_path: pytorch/torch/distributions/gamma.py
imports: from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.exp_family import ExponentialFamily
  from torch.distributions.utils import broadcast_all
label_desc: Use image node_id 11 for calling the Gamma obj's underlying member method code with example usage: obj.cdf(value) and returns: torch