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 _matmul(self, x, adjoint=False, adjoint_arg=False):
      x = ops.convert_to_tensor(x, name="x")
      return math_ops.matmul(
          self._matrix, x, adjoint_a=adjoint, adjoint_b=adjoint_arg
      )
  
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 4 for calling the LinearOperatorMatmulSolve obj's underlying member method code with example usage: obj._matmul(x, adjoint, adjoint_arg) and returns: math_ops