class_docstr: LinearOperator that wraps a [batch] matrix and implements matmul/solve.
class_docstr_tok: ["LinearOperator","that","wraps","a","[","batch","]","matrix","and","implements","matmul\/solve","."]
code: def __init__(
      self,
      matrix,
      is_non_singular=None,
      is_self_adjoint=None,
      is_positive_definite=None,
      is_square=None,
  ):
      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,
      )
  
      self._matrix = ops.convert_to_tensor(matrix, name="matrix")
      super(LinearOperatorMatmulSolve, 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,
      )
  
imports: import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.linalg import linalg
  from tensorflow.python.ops.parallel_for import control_flow_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 to create a new LinearOperatorMatmulSolve object from inherited base classes: linalg with example: obj = LinearOperatorMatmulSolve(matrix, is_non_singular, is__adjoint, is_positive_definite, is_square)