class_docstr: `LinearOperator` acting like a [batch] square diagonal matrix.
  
  This operator acts like a [batch] diagonal 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.  This matrix `A` is not materialized, but for
  purposes of broadcasting this shape will be relevant.
  
  `LinearOperatorDiag` is initialized with a (batch) vector.
  
  ```python
  # Create a 2 x 2 diagonal linear operator.
  diag = [1., -1.]
  operator = LinearOperatorDiag(diag)
  
  operator.to_dense()
  ==> [[1.,  0.]
       [0., -1.]]
  
  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.
  diag = tf.random.normal(shape=[2, 3, 4])
  operator = LinearOperatorDiag(diag)
  
  # Create a shape [2, 1, 4, 2] vector.  Note that this shape is compatible
  # since the batch dimensions, [2, 1], are broadcast to
  # operator.batch_shape = [2, 3].
  y = tf.random.normal(shape=[2, 1, 4, 2])
  x = operator.solve(y)
  ==> operator.matmul(x) = y
  ```
  
  #### Shape compatibility
  
  This operator acts on [batch] matrix with compatible shape.
  `x` is a batch matrix with compatible shape for `matmul` and `solve` if
  
  ```
  operator.shape = [B1,...,Bb] + [N, N],  with b >= 0
  x.shape =   [C1,...,Cc] + [N, R],
  and [C1,...,Cc] broadcasts with [B1,...,Bb] to [D1,...,Dd]
  ```
  
  #### Performance
  
  Suppose `operator` is a `LinearOperatorDiag` of shape `[N, N]`,
  and `x.shape = [N, R]`.  Then
  
  * `operator.matmul(x)` involves `N * R` multiplications.
  * `operator.solve(x)` involves `N` divisions and `N * R` multiplications.
  * `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.
code: def _matmul(self, x, adjoint=False, adjoint_arg=False):
      diag_term = math_ops.conj(self._diag) if adjoint else self._diag
      x = linalg.adjoint(x) if adjoint_arg else x
      diag_mat = array_ops.expand_dims(diag_term, -1)
      return diag_mat * x
  
imports: from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.linalg import linalg_impl
  from tensorflow.python.ops.linalg import linear_operator
  from tensorflow.python.ops.linalg import linear_operator_lower_triangular
  from tensorflow.python.ops.linalg import linear_operator_util
  from tensorflow.python.ops.linalg import property_hint_util
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 14 for calling the LinearOperatorDiag obj's underlying member method code with example usage: obj._matmul(x, adjoint, adjoint_arg) and returns: unknown