class_docstr: Shared logic for amplitude/length_scale parameterized kernels.
class_docstr_tok: ["Shared","logic","for","amplitude\/length_scale","parameterized","kernels","."]
code: def _parameter_properties(cls, dtype):
      from tensorflow_probability.python.bijectors import (
          softplus,
      )  # pylint:disable=g-import-not-at-top
  
      return dict(
          amplitude=parameter_properties.ParameterProperties(
              default_constraining_bijector_fn=(
                  lambda: softplus.Softplus(low=dtype_util.eps(dtype))
              )
          ),
          length_scale=parameter_properties.ParameterProperties(
              default_constraining_bijector_fn=(
                  lambda: softplus.Softplus(low=dtype_util.eps(dtype))
              )
          ),
          inverse_length_scale=parameter_properties.ParameterProperties(
              default_constraining_bijector_fn=softplus.Softplus
          ),
      )
  
imports: import numpy
  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 tensor_util
  from tensorflow_probability.python.math import bessel
  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 6 for calling the _AmplitudeLengthScaleMixin obj's underlying member method code with example usage: obj._parameter_properties(cls, dtype) and returns: dict