class_docstr: Compute `Y = g(X; scale) = scale @ X`.
  
  The `scale` term is presumed lower-triangular and non-singular (ie, no zeros
  on the diagonal), which permits efficient determinant calculation (linear in
  matrix dimension, instead of cubic).
  
  #### Examples
  
  ```python
  # Y = scale_tril @ X
  b = ScaleMatvecTriL(scale_tril=[[1., 0.], [1., 1.])
  ```
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code: def _parameter_properties(cls, dtype):
      # pylint: disable=g-long-lambda
      return dict(
          scale_tril=parameter_properties.ParameterProperties(
              event_ndims=2,
              shape_fn=lambda sample_shape: ps.concat(
                  [sample_shape, sample_shape[-1:]], axis=0
              ),
              default_constraining_bijector_fn=fill_triangular_bijector.FillTriangular,
          )
      )
  
file_path: probability/tensorflow_probability/python/bijectors/scale_matvec_tril.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import fill_triangular
  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 prefer_static
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 2 for calling the ScaleMatvecTriL obj's underlying member method code with example usage: obj._parameter_properties(cls, dtype) and returns: dict