class_docstr: A normal distribution object.
  
  A normal distribution models the probability of a variable occuring under
  a bell-shaped curve. It is described by a vector of mean values and a
  covariance value that can be zero, one, or two dimensional. This
  distribution can assume that features are independent of the others if
  the covariance type is 'diag' or 'sphere', but if the type is 'full' then
  the features are not independent.
  
  There are two ways to initialize this object. The first is to pass in
  the tensor of probablity 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 probability
  parameter will be learned from data.
  
  
  Parameters
  ----------
  means: list, numpy.ndarray, torch.Tensor or None, shape=(d,), optional
          The mean values of the distributions. Default is None.
  
  covs: list, numpy.ndarray, torch.Tensor, or None, optional
          The variances and covariances of the distribution. If covariance_type
          is 'full', the shape should be (self.d, self.d); if 'diag', the shape
          should be (self.d,); if 'sphere', it should be (1,). Note that this is
          the variances or covariances in all settings, and not the standard
          deviation, as may be more common for diagonal covariance matrices.
          Default is None.
  
  covariance_type: str, optional
          The type of covariance matrix. Must be one of 'full', 'diag', or
          'sphere'. Default is 'full'. 
  
  min_cov: float or None, optional
          The minimum variance or covariance.
  
  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.
  
  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.
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code: def __init__(
      self,
      means=None,
      covs=None,
      covariance_type="full",
      min_cov=None,
      inertia=0.0,
      frozen=False,
      check_data=True,
  ):
      super().__init__(
          inertia=inertia, frozen=frozen, check_data=check_data
      )
      self.name = "Normal"
  
      self.means = _check_parameter(
          _cast_as_parameter(means), "means", ndim=1
      )
      self.covs = _check_parameter(
          _cast_as_parameter(covs), "covs", ndim=(1, 2)
      )
  
      _check_shapes([self.means, self.covs], ["means", "covs"])
  
      self.min_cov = _check_parameter(
          min_cov, "min_cov", min_value=0, ndim=0
      )
      self.covariance_type = covariance_type
  
      self._initialized = (means is not None) and (covs is not None)
      self.d = self.means.shape[-1] if self._initialized else None
      self._reset_cache()
  
file_path: pomegranate/pomegranate/distributions/normal.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 1 to create a new Normal object from inherited base classes: Distribution with example: obj = Normal(means, covs, covariance_type, min_cov, inertia, frozen, check_data)