class_docstr: `LinearOperator` acting like a [batch] square lower triangular matrix.
  
  This operator acts like a [batch] lower triangular matrix `A` with shape
  `[B1,...,Bb, N, N]` for some `b >= 0`.  The first `b` indices index a
  batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is
  an `N x N` matrix.
  
  `LinearOperatorLowerTriangular` is initialized with a `Tensor` having
  dimensions `[B1,...,Bb, N, N]`. The upper triangle of the last two
  dimensions is ignored.
  
  ```python
  # Create a 2 x 2 lower-triangular linear operator.
  tril = [[1., 2.], [3., 4.]]
  operator = LinearOperatorLowerTriangular(tril)
  
  # The upper triangle is ignored.
  operator.to_dense()
  ==> [[1., 0.]
       [3., 4.]]
  
  tensor_shape.TensorShape(operator.shape)
  ==> [2, 2]
  
  operator.log_abs_determinant()
  ==> scalar Tensor
  
  x = ... Shape [2, 4] Tensor
  operator.matmul(x)
  ==> Shape [2, 4] Tensor
  
  # Create a [2, 3] batch of 4 x 4 linear operators.
  tril = tf.random.normal(shape=[2, 3, 4, 4])
  operator = LinearOperatorLowerTriangular(tril)
  ```
  
  #### Shape compatibility
  
  This operator acts on [batch] matrix with compatible shape.
  `x` is a batch matrix with compatible shape for `matmul` and `solve` if
  
  ```
  tensor_shape.TensorShape(operator.shape) = [B1,...,Bb] + [N, N],  with b >= 0
  tensor_shape.TensorShape(x.shape) =        [B1,...,Bb] + [N, R],  with R >= 0.
  ```
  
  #### Performance
  
  Suppose `operator` is a `LinearOperatorLowerTriangular` of shape `[N, N]`,
  and `tensor_shape.TensorShape(x.shape) = [N, R]`.  Then
  
  * `operator.matmul(x)` involves `N^2 * R` multiplications.
  * `operator.solve(x)` involves `N * R` size `N` back-substitutions.
  * `operator.determinant()` involves a size `N` `reduce_prod`.
  
  If instead `operator` and `x` have shape `[B1,...,Bb, N, N]` and
  `[B1,...,Bb, N, R]`, every operation increases in complexity by `B1*...*Bb`.
  
  #### Matrix property hints
  
  This `LinearOperator` is initialized with boolean flags of the form `is_X`,
  for `X = non_singular, self_adjoint, positive_definite, square`.
  These have the following meaning:
  
  * If `is_X == True`, callers should expect the operator to have the
    property `X`.  This is a promise that should be fulfilled, but is *not* a
    runtime assert.  For example, finite floating point precision may result
    in these promises being violated.
  * If `is_X == False`, callers should expect the operator to not have `X`.
  * If `is_X == None` (the default), callers should have no expectation either
    way.
docstr: Gets the `tril` kwarg, with upper part zero-d out.
docstr_tok: ["Gets","the","`","tril","`","kwarg",",","with","upper","part","zero-d","out","."]
code: def _get_tril(self):
      """Gets the `tril` kwarg, with upper part zero-d out."""
      return _linalg.band_part(self._tril, -1, 0)
  
imports: from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy import numpy_array
  from tensorflow_probability.python.internal.backend.numpy import numpy_math
  from tensorflow_probability.python.internal.backend.numpy import linalg_impl
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator_util
  from tensorflow_probability.python.internal.backend.numpy.gen import property_hint_util
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import linalg_impl
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy.gen import tensor_shape
  from tensorflow_probability.python.internal.backend.numpy import private
label_desc: Use image node_id 4 for calling the LinearOperatorLowerTriangular obj's underlying member method code with example usage: obj._get_tril() and returns: _linalg