class_docstr: The GammaExponential kernel.
  
  A generalization of the `ExponentiatedQuadratic` ("squared exponential",
  "Gaussian" or "radial basis function") kernel, this kernel takes the form
  
    ```none
    k(x, y) = amplitude**2 * exp(
        -(||x - y||**2 / (2 * length_scale**2))**power)
    ```
  
  where the double-bars represent vector length (ie, Euclidean, or L2 norm).
  
  The `power` parameter is sometimes referred to as gamma, or as gamma / 2.
  This parameter controls the shape of the kernel.
class_docstr_tok: ["The","GammaExponential","kernel",".","A","generalization","of","the","`","ExponentiatedQuadratic","`","(","``","squared","exponential","''",",","''","Gaussian","''","or","``","radial","basis","function","''",")","kernel",",","this","kernel","takes","the","form","``","`","none","k","(","x",",","y",")","=","amplitude","*","*","2","*","exp","(","-","(","||x","-","y||","*","*","2","\/","(","2","*","length_scale","*","*","2",")",")","*","*","power",")","``","`","where","the","double-bars","represent","vector","length","(","ie",",","Euclidean",",","or","L2","norm",")",".","The","`","power","`","parameter","is","sometimes","referred","to","as","gamma",",","or","as","gamma","\/","2",".","This","parameter","controls","the","shape","of","the","kernel","."]
code: def _parameter_control_dependencies(self, is_init):
      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_length_scale` must be non-negative.",
              )
          )
      if self._power is not None and is_init != tensor_util.is_ref(
          self._power
      ):
          power = tf.convert_to_tensor(self._power)
          assertions.append(
              assert_util.assert_positive(
                  power, message="`power` must be positive."
              )
          )
          assertions.append(
              assert_util.assert_less_equal(
                  power,
                  tf.ones([], dtype=self.dtype),
                  message="`power` must be <= 1.",
              )
          )
      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 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 positive_semidefinite_kernel
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 12 for calling the GammaExponential obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list