class_docstr: Multivariate normal that forces some sample dimensions to zero.
  
  This is equivalent to setting `loc[d] = 0.` and `precision_factor[d, d]=`inf`
  in the zeroed dimensions, but is numerically better behaved.
class_docstr_tok: ["Multivariate","normal","that","forces","some","sample","dimensions","to","zero",".","This","is","equivalent","to","setting","`","loc","[","d","]","=","0.","`","and","`","precision_factor","[","d",",","d","]","=","`","inf","`","in","the","zeroed","dimensions",",","but","is","numerically","better","behaved","."]
code: def __init__(self, loc, precision_factor, nonzeros, **kwargs):
      self._nonzeros = nonzeros
      super().__init__(
          loc=loc, precision_factor=precision_factor, **kwargs
      )
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.experimental.distributions.mvn_precision_factor_linop import MultivariateNormalPrecisionFactorLinearOperator
  from tensorflow_probability.python.internal import broadcast_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import vectorization_util
  from tensorflow_probability.python.math import linalg
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 1 to create a new MVNPrecisionFactorHardZeros object from inherited base classes: MultivariateNormalPrecisionFactorLinearOperator with example: obj = MVNPrecisionFactorHardZeros(loc, precision_factor, nonzeros)