def tukey_h_params(x: common_types.TensorType,
                   reduce_instance_dims: bool = True,
                   output_dtype: Optional[tf.DType] = None,
                   name: Optional[str] = None) -> Tuple[tf.Tensor, tf.Tensor]:
  """Computes the h parameters of the values of a `Tensor` over the dataset.

  This computes the parameters (hl, hr) of the samples, assuming a Tukey HH
  distribution, i.e. (x - tukey_location) / tukey_scale is a Tukey HH
  distribution with parameters hl (left parameter) and hr (right parameter).
  See the following publication for the definition of the Tukey HH distribution:

  Todd C. Headrick, and Mohan D. Pant. "Characterizing Tukey h and
  hh-Distributions through L-Moments and the L-Correlation," ISRN Applied
  Mathematics, vol. 2012, 2012. doi:10.5402/2012/980153

  Args:
    x: A `Tensor`, `SparseTensor`, or `RaggedTensor`. Its type must be floating
        point (float{16|32|64}), or integral ([u]int{8|16|32|64}).
    reduce_instance_dims: By default collapses the batch and instance dimensions
        to arrive at a single scalar output. If False, only collapses the batch
        dimension and outputs a vector of the same shape as the input.
    output_dtype: (Optional) If not None, casts the output tensor to this type.
    name: (Optional) A name for this operation.

  Returns:
    The tuple (hl, hr) containing two `Tensor` instances with the hl and hr
    parameters. If `x` is floating point, each parameter will have the same type
    as `x`. If `x` is integral, the output is cast to float32.

  Raises:
    TypeError: If the type of `x` is not supported.
  """
  with tf.compat.v1.name_scope(name, 'tukey_h_params'):
    return _tukey_parameters(x, reduce_instance_dims, output_dtype)[2:]
Computes the h parameters of the values of a `Tensor` over the dataset.

  This computes the parameters (hl, hr) of the samples, assuming a Tukey HH
  distribution, i.e. (x - tukey_location) / tukey_scale is a Tukey HH
  distribution with parameters hl (left parameter) and hr (right parameter).
  See the following publication for the definition of the Tukey HH distribution:

  Todd C. Headrick, and Mohan D. Pant. "Characterizing Tukey h and
  hh-Distributions through L-Moments and the L-Correlation," ISRN Applied
  Mathematics, vol. 2012, 2012. doi:10.5402/2012/980153

  Args:
    x: A `Tensor`, `SparseTensor`, or `RaggedTensor`. Its type must be floating
        point (float{16|32|64}), or integral ([u]int{8|16|32|64}).
    reduce_instance_dims: By default collapses the batch and instance dimensions
        to arrive at a single scalar output. If False, only collapses the batch
        dimension and outputs a vector of the same shape as the input.
    output_dtype: (Optional) If not None, casts the output tensor to this type.
    name: (Optional) A name for this operation.

  Returns:
    The tuple (hl, hr) containing two `Tensor` instances with the hl and hr
    parameters. If `x` is floating point, each parameter will have the same type
    as `x`. If `x` is integral, the output is cast to float32.

  Raises:
    TypeError: If the type of `x` is not supported.
  
tensorflow_transform/analyzers.py