class_docstr: `LinearOperator` that wraps a [batch] matrix.
  
  This operator wraps a [batch] matrix `A` (which is a `Tensor`) with shape
  `[B1,...,Bb, M, N]` for some `b >= 0`.  The first `b` indices index a
  batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is
  an `M x N` matrix.
  
  ```python
  # Create a 2 x 2 linear operator.
  matrix = [[1., 2.], [3., 4.]]
  operator = LinearOperatorFullMatrix(matrix)
  
  operator.to_dense()
  ==> [[1., 2.]
       [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.
  matrix = tf.random.normal(shape=[2, 3, 4, 4])
  operator = LinearOperatorFullMatrix(matrix)
  ```
  
  #### 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] + [M, N],  with b >= 0
  tensor_shape.TensorShape(x.shape) =        [B1,...,Bb] + [N, R],  with R >= 0.
  ```
  
  #### Performance
  
  `LinearOperatorFullMatrix` has exactly the same performance as would be
  achieved by using standard `TensorFlow` matrix ops.  Intelligent choices are
  made based on the following initialization hints.
  
  * If `dtype` is real, and `is_self_adjoint` and `is_positive_definite`, a
    Cholesky factorization is used for the determinant and solve.
  
  In all cases, suppose `operator` is a `LinearOperatorFullMatrix` of shape
  `[M, N]`, and `tensor_shape.TensorShape(x.shape) = [N, R]`.  Then
  
  * `operator.matmul(x)` is `O(M * N * R)`.
  * If `M=N`, `operator.solve(x)` is `O(N^3 * R)`.
  * If `M=N`, `operator.determinant()` is `O(N^3)`.
  
  If instead `operator` and `x` have shape `[B1,...,Bb, M, 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 _shape(self):
      return tensor_shape.TensorShape(self._matrix.shape)
  
imports: from tensorflow_probability.python.internal.backend.numpy import dtype
  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.gen import linear_operator
  from tensorflow_probability.python.internal.backend.numpy.gen import linear_operator_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 LinearOperatorFullMatrix obj's underlying member method code with example usage: obj._shape() and returns: tensor_shape