def __init__(self,
               bias_variance=None,
               slope_variance=None,
               shift=None,
               feature_ndims=1,
               validate_args=False,
               name='Linear'):
    """Construct a Linear kernel instance.

    Args:
      bias_variance: Positive floating point `Tensor` that controls the
        variance from the origin. If bias = 0, there is no variance and the
        fitted function goes through the origin (also known as the homogeneous
        linear kernel). Must be broadcastable with `slope_variance`,
        `shift` and inputs to `apply` and `matrix` methods. A value of
        `None` is treated like 0.
        Default Value: `None`
      slope_variance: Positive floating point `Tensor` that controls the
        variance of the regression line slope. Must be broadcastable with
        `bias_variance`, `shift`, and inputs to `apply` and `matrix`
        methods. A value of `None` is treated like 1.
        Default Value: `None`
      shift: Floating point `Tensor` that controls the intercept with the
        x-axis of the linear interpolation. Must be broadcastable with
        `bias_variance`, `slope_variance`, and inputs to `apply` and `matrix`
        methods. A value of `None` is treated like 0, which results in having
        the intercept at the origin.
      feature_ndims: Python `int` number of rightmost dims to include in
        kernel computation.
        Default Value: 1
      validate_args: If `True`, parameters are checked for validity despite
        possibly degrading runtime performance.
        Default Value: `False`
      name: Python `str` name prefixed to Ops created by this class.
        Default Value: `'Linear'`
    """
    super(Linear, self).__init__(
        bias_variance=bias_variance,
        slope_variance=slope_variance,
        shift=shift,
        exponent=None,
        feature_ndims=feature_ndims,
        validate_args=validate_args,
        name=name)
def __init__ ( self , bias_variance = None , slope_variance = None , shift = None , feature_ndims = 1 , validate_args = False , name = 'Linear' ) : super ( Linear , self ) . __init__ ( bias_variance = bias_variance , slope_variance = slope_variance , shift = shift , exponent = None , feature_ndims = feature_ndims , validate_args = validate_args , name = name )
Construct a Linear kernel instance.

    Args:
      bias_variance: Positive floating point `Tensor` that controls the
        variance from the origin. If bias = 0, there is no variance and the
        fitted function goes through the origin (also known as the homogeneous
        linear kernel). Must be broadcastable with `slope_variance`,
        `shift` and inputs to `apply` and `matrix` methods. A value of
        `None` is treated like 0.
        Default Value: `None`
      slope_variance: Positive floating point `Tensor` that controls the
        variance of the regression line slope. Must be broadcastable with
        `bias_variance`, `shift`, and inputs to `apply` and `matrix`
        methods. A value of `None` is treated like 1.
        Default Value: `None`
      shift: Floating point `Tensor` that controls the intercept with the
        x-axis of the linear interpolation. Must be broadcastable with
        `bias_variance`, `slope_variance`, and inputs to `apply` and `matrix`
        methods. A value of `None` is treated like 0, which results in having
        the intercept at the origin.
      feature_ndims: Python `int` number of rightmost dims to include in
        kernel computation.
        Default Value: 1
      validate_args: If `True`, parameters are checked for validity despite
        possibly degrading runtime performance.
        Default Value: `False`
      name: Python `str` name prefixed to Ops created by this class.
        Default Value: `'Linear'`