class_docstr: Shared logic for amplitude/length_scale parameterized kernels.
class_docstr_tok: ["Shared","logic","for","amplitude\/length_scale","parameterized","kernels","."]
docstr: Control dependencies for parameters.
code: def _parameter_control_dependencies(self, is_init):
      """Control dependencies for parameters."""
      if not self.validate_args:
          return []
      assertions = []
      if (
          self._inverse_length_scale is not None
          and is_init != tensor_util.is_ref(self._inverse_length_scale)
      ):
          assertions.append(
              assert_util.assert_non_negative(
                  self._inverse_length_scale,
                  message="`inverse_scale_diag` must be non-negative.",
              )
          )
      for arg_name, arg in dict(
          amplitude=self.amplitude, length_scale=self._length_scale
      ).items():
          if arg is not None and is_init != tensor_util.is_ref(arg):
              assertions.append(
                  assert_util.assert_positive(
                      arg, message=f"{arg_name} must be positive."
                  )
              )
      return assertions
  
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 7 for calling the _AmplitudeLengthScaleMixin obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list