class_docstr: Schur complement kernel, damped by scalar factors.
  
  This kernel is the same as the SchurComplement kernel, except we multiply by
  the following factor:
  
  `(df + b - 2) / (df + n - 2)`, where:
  
    * `df` is the degrees of freedom parameter.
    * `n` is the number of observations for `fixed_inputs`.
    * `b` is `||divisor_matrix_cholesky^-1 fixed_inputs_observations|| ** 2`.
class_docstr_tok: ["Schur","complement","kernel",",","damped","by","scalar","factors",".","This","kernel","is","the","same","as","the","SchurComplement","kernel",",","except","we","multiply","by","the","following","factor",":","`","(","df","+","b","-","2",")","\/","(","df","+","n","-","2",")","`",",","where",":","*","`","df","`","is","the","degrees","of","freedom","parameter",".","*","`","n","`","is","the","number","of","observations","for","`","fixed_inputs","`",".","*","`","b","`","is","`","||divisor_matrix_cholesky^-1","fixed_inputs_observations||","*","*","2","`","."]
code: def _parameter_properties(cls, dtype, num_classes=None):
      return dict(
          df=parameter_properties.ParameterProperties(
              default_constraining_bijector_fn=(
                  lambda: softplus_bijector.Softplus(  # pylint:disable=g-long-lambda
                      low=dtype_util.as_numpy_dtype(dtype)(2.0)
                  )
              )
          ),
          schur_complement=parameter_properties.BatchedComponentProperties(),
          observations_is_missing=parameter_properties.ParameterProperties(
              event_ndims=1,
              shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED,
          ),
          fixed_inputs_observations=parameter_properties.ParameterProperties(
              event_ndims=1,
              shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED,
          ),
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python import util
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import cholesky_util
  from tensorflow_probability.python.distributions import student_t_process
  from tensorflow_probability.python.distributions.internal import stochastic_process_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
  from tensorflow_probability.python.math.psd_kernels import schur_complement
  from tensorflow.python.util import deprecation
label_desc: Use image node_id 2 for calling the DampedSchurComplement obj's underlying member method code with example usage: obj._parameter_properties(cls, dtype, num_classes) and returns: dict