class_docstr: Compute the reflection of a vector across a hyperplane.
  
  The reflection of x across the hyperplane with unit normal vector `v`
  (the `reflection_axis`) is `x - 2 * <x, v> * v`.
  
  
  #### Examples
  
  ```python
  householder = tfp.bijectors.Householder(reflection_axis = [-0.8, 0.6])
  x = [1.0, 0.0]
  householder.forward(x)  # [-0.28, 0.96]
  ```
  
  **Note:** The norm of `reflection_axis` should be 1. If the norm
  is less than 1e-6, results may not be accurate.
docstr: Instantiates the `Householder` bijector.
  
  Args:
    reflection_axis: Vector normal to the reflection hyperplane.
    validate_args: Python `bool` indicating whether arguments should be
      checked for correctness.
    name: Python `str` name given to ops managed by this object.
docstr_tok: ["Instantiates","the","`","Householder","`","bijector",".","Args",":","reflection_axis",":","Vector","normal","to","the","reflection","hyperplane",".","validate_args",":","Python","`","bool","`","indicating","whether","arguments","should","be","checked","for","correctness",".","name",":","Python","`","str","`","name","given","to","ops","managed","by","this","object","."]
code: def __init__(
      self, reflection_axis, validate_args=False, name="householder"
  ):
      """Instantiates the `Householder` bijector.
  
      Args:
        reflection_axis: Vector normal to the reflection hyperplane.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype(
              [reflection_axis], dtype_hint=tf.float32
          )
          self._reflection_axis = tensor_util.convert_nonref_to_tensor(
              reflection_axis, dtype=dtype, name="reflection_axis"
          )
          super(Householder, self).__init__(
              forward_min_event_ndims=1,
              is_constant_jacobian=True,
              dtype=dtype,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
file_path: probability/tensorflow_probability/python/bijectors/householder.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import scale_matvec_linear_operator
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 to create a new Householder object from inherited base classes: bijector with example: obj = Householder(reflection_axis, validate_args, name)