class_docstr: ``DatasetAttribute`` for ``pennylane.operation.Operator`` classes.
  
  Supports all operator types that meet the following conditions:
      - The ``__init__()`` method matches the signature of ``Operator.__init__``,
          or any additional arguments are optional and do not affect the value of
          the operator
      - The ``data`` and ``wires`` attributes will produce an identical copy of
          operator if passed into the classes' ``__init__()`` method. Generally,
          this means ``__init__()`` do not mutate the ``identifiers`` and ``wires``
          arguments.
      - Hyperparameters are not used or are automatically derived by ``__init__()``.
  
  Almost all operators meet these conditions. This type also supports serializing the
  ``Hamiltonian`` and ``Tensor`` operators.
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docstr: Load list of serialized ops from ``bind``.
docstr_tok: ["Load","list","of","serialized","ops","from","``","bind","``","."]
code: def _hdf5_to_ops(self, bind: HDF5Group) -> List[Operator]:
      """Load list of serialized ops from ``bind``."""
      ops = []
  
      names_bind = bind["op_class_names"]
      wires_bind = bind["op_wire_labels"]
      op_class_names = (
          [] if names_bind.shape == (0,) else names_bind.asstr()
      )
      op_wire_labels = (
          [] if wires_bind.shape == (0,) else wires_bind.asstr()
      )
  
      with qml.QueuingManager.stop_recording():
          for i, op_class_name in enumerate(op_class_names):
              op_key = f"op_{i}"
  
              op_cls = self._supported_ops_dict()[op_class_name]
              if op_cls is Tensor:
                  ops.append(Tensor(*self._hdf5_to_ops(bind[op_key])))
              elif op_cls is qml.Hamiltonian:
                  ops.append(
                      qml.Hamiltonian(
                          coeffs=list(
                              bind[op_key]["hamiltonian_coeffs"]
                          ),
                          observables=self._hdf5_to_ops(bind[op_key]),
                      )
                  )
              else:
                  wire_labels = json.loads(op_wire_labels[i])
                  op_data = bind[op_key]
                  if op_data.shape is not None:
                      params = np.zeros(
                          shape=op_data.shape, dtype=op_data.dtype
                      )
                      op_data.read_direct(params)
                      ops.append(op_cls(*params, wires=wire_labels))
                  else:
                      ops.append(op_cls(wires=wire_labels))
  
      return ops
  
file_path: pennylane/pennylane/data/attributes/operator/operator.py
imports: import json
  import typing
  from functools import lru_cache
  from typing import Dict, FrozenSet, Generic, List, Type, TypeVar
  import numpy
  import pennylane
  from pennylane.data.base.attribute import DatasetAttribute
  from pennylane.data.base.hdf5 import HDF5Group, h5py
  from pennylane.operation import Operator, Tensor
  from ._wires import wires_to_json
label_desc: Use image node_id 5 for calling the DatasetOperator obj's underlying member method code with example usage: obj._hdf5_to_ops(bind) and returns: ops