class_docstr: Kernel that first rescales all feature dimensions.
  
  Given a kernel `k` and `scale_diag` and inputs `x` and `y`, this kernel
  first rescales the input by computing `x / scale_diag` and
  `y / scale_diag`, and passing this to `k`.
  
  With 1 feature dimension, this is also called Automatic Relevance
  Determination (ARD) [1].
  
  #### References
  [1]: Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian
       Processes for Machine Learning. Section 5.1 2006.
       http://www.gaussianprocess.org/gpml/chapters/RW5.pdf
class_docstr_tok: ["Kernel","that","first","rescales","all","feature","dimensions",".","Given","a","kernel","`","k","`","and","`","scale_diag","`","and","inputs","`","x","`","and","`","y","`",",","this","kernel","first","rescales","the","input","by","computing","`","x","\/","scale_diag","`","and","`","y","\/","scale_diag","`",",","and","passing","this","to","`","k","`",".","With","1","feature","dimension",",","this","is","also","called","Automatic","Relevance","Determination","(","ARD",")","[","1","]",".","#","#","#","#","References","[","1","]",":","Carl","Edward","Rasmussen","and","Christopher","K.","I.","Williams",".","Gaussian","Processes","for","Machine","Learning",".","Section","5.1","2006.","http",":","\/\/www.gaussianprocess.org\/gpml\/chapters\/RW5.pdf"]
code: def _parameter_control_dependencies(self, is_init):
      if not self.validate_args:
          return []
      assertions = []
      if (
          self._inverse_scale_diag is not None
          and is_init != tensor_util.is_ref(self._inverse_scale_diag)
      ):
          assertions.append(
              assert_util.assert_non_negative(
                  self._inverse_scale_diag,
                  message="`inverse_scale_diag` must be non-negative.",
              )
          )
      if (
          self._scale_diag is not None
          and is_init != tensor_util.is_ref(self._scale_diag)
      ):
          assertions.append(
              assert_util.assert_positive(
                  self._scale_diag,
                  message="`scale_diag` must be positive.",
              )
          )
      return assertions
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/feature_scaled.py
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 tensor_util
  from tensorflow_probability.python.math.psd_kernels import feature_transformed
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 5 for calling the FeatureScaled obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list