class_docstr: Kernel that just outputs positive constant values.
  
  Useful class for multiplying / adding constants with other kernels.
  
  Warning: This can potentially lead to poorly conditioned matrices, since
  if the constant is large, adding it to each entry of another matrix will
  make it closer to an ill-conditioned matrix. If using a `Constant` kernel
  as a summand for a composite kernel in a `GaussianProcess`, it's instead
  recommended to use a trainable mean instead.
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code: def _parameter_properties(cls, dtype):
      from tensorflow_probability.python.bijectors import (
          softplus,
      )  # pylint:disable=g-import-not-at-top
  
      return dict(
          constant=parameter_properties.ParameterProperties(
              default_constraining_bijector_fn=(
                  lambda: softplus.Softplus(low=dtype_util.eps(dtype))
              )
          )
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_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 tensor_util
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 3 for calling the Constant obj's underlying member method code with example usage: obj._parameter_properties(cls, dtype) and returns: dict