class_docstr: A gamma distribution object.
  
  A gamma distribution is the sum of exponential distributions, and has shape
  and rate parameters. This distribution assumes that each feature is
  independent of the others. 
  
  There are two ways to initialize this objecct. The first is to pass in
  the tensor of rate and shae parameters, at which point they can immediately 
  be used. The second is to not pass in the rate parameters and then call
  either `fit` or `summary` + `from_summaries`, at which point the rate
  and shape parameters will be learned from data.
  
  
  Parameters
  ----------
  shapes: torch.tensor or None, shape=(d,), optional
          The shape parameter for each feature. Default is None
  
  rates: torch.tensor or None, shape=(d,), optional
          The rate parameters for each feature. Default is None.
  
  inertia: float, (0, 1), optional
          Indicates the proportion of the update to apply to the parameters
          during training. When the inertia is 0.0, the update is applied in
          its entirety and the previous parameters are ignored. When the
          inertia is 1.0, the update is entirely ignored and the previous
          parameters are kept, equivalently to if the parameters were frozen.
  
  tol: float, [0, inf), optional
          The threshold at which to stop fitting the parameters of the
          distribution. Default is 1e-4.
  
  max_iter: int, [0, inf), optional
          The maximum number of iterations to run EM when fitting the parameters
          of the distribution. Default is 20.
  
  frozen: bool, optional
          Whether all the parameters associated with this distribution are frozen.
          If you want to freeze individual pameters, or individual values in those
          parameters, you must modify the `frozen` attribute of the tensor or
          parameter directly. Default is False.
  
  check_data: bool, optional
          Whether to check properties of the data and potentially recast it to
          torch.tensors. This does not prevent checking of parameters but can
          slightly speed up computation when you know that your inputs are valid.
          Setting this to False is also necessary for compiling.
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docstr: Extract the sufficient statistics from a batch of data.
  
  This method calculates the sufficient statistics from optionally
  weighted data and adds them to the stored cache. The examples must be
  given in a 2D format. Sample weights can either be provided as one
  value per example or as a 2D matrix of weights for each feature in
  each example.
  
  
  Parameters
  ----------
  X: list, tuple, numpy.ndarray, torch.Tensor, shape=(-1, self.d)
          A set of examples to summarize.
  
  sample_weight: list, tuple, numpy.ndarray, torch.Tensor, optional
          A set of weights for the examples. This can be either of shape
          (-1, self.d) or a vector of shape (-1,). Default is ones.
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code: def summarize(self, X, sample_weight=None):
      """Extract the sufficient statistics from a batch of data.
  
      This method calculates the sufficient statistics from optionally
      weighted data and adds them to the stored cache. The examples must be
      given in a 2D format. Sample weights can either be provided as one
      value per example or as a 2D matrix of weights for each feature in
      each example.
  
  
      Parameters
      ----------
      X: list, tuple, numpy.ndarray, torch.Tensor, shape=(-1, self.d)
              A set of examples to summarize.
  
      sample_weight: list, tuple, numpy.ndarray, torch.Tensor, optional
              A set of weights for the examples. This can be either of shape
              (-1, self.d) or a vector of shape (-1,). Default is ones.
      """
  
      if self.frozen == True:
          return
  
      X, sample_weight = super().summarize(
          X, sample_weight=sample_weight
      )
      _check_parameter(
          X, "X", min_value=0, check_parameter=self.check_data
      )
  
      self._w_sum[:] = self._w_sum + torch.sum(sample_weight, dim=0)
      self._xw_sum[:] = self._xw_sum + torch.sum(
          X * sample_weight, dim=0
      )
      self._logx_w_sum[:] = self._logx_w_sum + torch.sum(
          torch.log(X) * sample_weight, dim=0
      )
  
file_path: pomegranate/pomegranate/distributions/gamma.py
imports: import torch
  from .._utils import _cast_as_tensor
  from .._utils import _cast_as_parameter
  from .._utils import _update_parameter
  from .._utils import _check_parameter
  from .._utils import _check_shapes
  from ._distribution import Distribution
label_desc: Use image node_id 6 for calling the Gamma obj's underlying member method code with example usage: obj.summarize(X, sample_weight) without return types