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.
docstr: Initialize a `LinearOperatorFullMatrix`.
  
  Args:
    matrix:  Shape `[B1,...,Bb, M, N]` with `b >= 0`, `M, N >= 0`.
      Allowed dtypes: `float16`, `float32`, `float64`, `complex64`,
      `complex128`.
    is_non_singular:  Expect that this operator is non-singular.
    is_self_adjoint:  Expect that this operator is equal to its hermitian
      transpose.
    is_positive_definite:  Expect that this operator is positive definite,
      meaning the quadratic form `x^H A x` has positive real part for all
      nonzero `x`.  Note that we do not require the operator to be
      self-adjoint to be positive-definite.  See:
      https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices
    is_square:  Expect that this operator acts like square [batch] matrices.
    name: A name for this `LinearOperator`.
  
  Raises:
    TypeError:  If `diag.dtype` is not an allowed type.
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code: def __init__(
      self,
      matrix,
      is_non_singular=None,
      is_self_adjoint=None,
      is_positive_definite=None,
      is_square=None,
      name="LinearOperatorFullMatrix",
  ):
      r"""Initialize a `LinearOperatorFullMatrix`.
  
      Args:
        matrix:  Shape `[B1,...,Bb, M, N]` with `b >= 0`, `M, N >= 0`.
          Allowed dtypes: `float16`, `float32`, `float64`, `complex64`,
          `complex128`.
        is_non_singular:  Expect that this operator is non-singular.
        is_self_adjoint:  Expect that this operator is equal to its hermitian
          transpose.
        is_positive_definite:  Expect that this operator is positive definite,
          meaning the quadratic form `x^H A x` has positive real part for all
          nonzero `x`.  Note that we do not require the operator to be
          self-adjoint to be positive-definite.  See:
          https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices
        is_square:  Expect that this operator acts like square [batch] matrices.
        name: A name for this `LinearOperator`.
  
      Raises:
        TypeError:  If `diag.dtype` is not an allowed type.
      """
      parameters = dict(
          matrix=matrix,
          is_non_singular=is_non_singular,
          is_self_adjoint=is_self_adjoint,
          is_positive_definite=is_positive_definite,
          is_square=is_square,
          name=name,
      )
  
      with ops.name_scope(name, values=[matrix]):
          self._matrix = linear_operator_util.convert_nonref_to_tensor(
              matrix, name="matrix"
          )
          self._check_matrix(self._matrix)
  
          super(LinearOperatorFullMatrix, self).__init__(
              dtype=self._matrix.dtype,
              is_non_singular=is_non_singular,
              is_self_adjoint=is_self_adjoint,
              is_positive_definite=is_positive_definite,
              is_square=is_square,
              parameters=parameters,
              name=name,
          )
  
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 1 to create a new LinearOperatorFullMatrix object from inherited base classes: linear_operator with example: obj = LinearOperatorFullMatrix(matrix, is_non_singular, is__adjoint, is_positive_definite, is_square, name)