code: def execute(cls, ctx, op):
      (a, b), device_id, xp = as_same_device(
          [ctx[c.key] for c in op.inputs],
          device=op.device,
          ret_extra=True,
      )
  
      with device(device_id):
          if not op.sparse and is_sparse_module(xp):
              # tell sparse to do calculation on numpy or cupy matmul
              ctx[op.outputs[0].key] = xp.matmul(a, b, sparse=False)
          else:
              try:
                  # `np.matmul` support `order` argument in version 1.16
                  ctx[op.outputs[0].key] = xp.matmul(
                      a, b, casting=op.casting, order=op.order
                  )
              except TypeError:  # pragma: no cover
                  ctx[op.outputs[0].key] = xp.matmul(a, b).astype(
                      dtype=op.dtype, casting=op.casting, order=op.order
                  )
  
imports: import itertools
  import numpy
  from ...None import opcodes
  from ...serialization.serializables import KeyField, StringField
  from ...utils import has_unknown_shape
  from ..core import Tensor, TensorOrder
  from ..utils import broadcast_shape, check_out_param, unify_chunks, check_order
  from ..array_utils import device, as_same_device, is_sparse_module
  from ..operands import TensorOperand, TensorOperandMixin
  from ..arithmetic.utils import chunk_tree_add
  from ..datasource import tensor
label_desc: Use image node_id 10 for calling the TensorMatmul obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types