code: def generate_param_groups(
      network: torch.nn.Module,
      layer_matches: Sequence[Callable],
      match_types: Sequence[str],
      lr_values: Sequence[float],
      include_others: bool = True,
  ) -> list[dict]:
      """
      Utility function to generate parameter groups with different LR values for optimizer.
      The output parameter groups have the same order as `layer_match` functions.
  
      Args:
          network: source network to generate parameter groups from.
          layer_matches: a list of callable functions to select or filter out network layer groups,
              for "select" type, the input will be the `network`, for "filter" type,
              the input will be every item of `network.named_parameters()`.
              for "select", the parameters will be
              `select_func(network).parameters()`.
              for "filter", the parameters will be
              `(x[1] for x in filter(f, network.named_parameters()))`
          match_types: a list of tags to identify the matching type corresponding to the `layer_matches` functions,
              can be "select" or "filter".
          lr_values: a list of LR values corresponding to the `layer_matches` functions.
          include_others: whether to include the rest layers as the last group, default to True.
  
      It's mainly used to set different LR values for different network elements, for example:
  
      .. code-block:: python
  
          net = Unet(spatial_dims=3, in_channels=1, out_channels=3, channels=[2, 2, 2], strides=[1, 1, 1])
          print(net)  # print out network components to select expected items
          print(net.named_parameters())  # print out all the named parameters to filter out expected items
          params = generate_param_groups(
              network=net,
              layer_matches=[lambda x: x.model[0], lambda x: "2.0.conv" in x[0]],
              match_types=["select", "filter"],
              lr_values=[1e-2, 1e-3],
          )
          # the groups will be a list of dictionaries:
          # [{'params': <generator object Module.parameters at 0x7f9090a70bf8>, 'lr': 0.01},
          #  {'params': <filter object at 0x7f9088fd0dd8>, 'lr': 0.001},
          #  {'params': <filter object at 0x7f9088fd0da0>}]
          optimizer = torch.optim.Adam(params, 1e-4)
  
      """
      layer_matches = ensure_tuple(layer_matches)
      match_types = ensure_tuple_rep(match_types, len(layer_matches))
      lr_values = ensure_tuple_rep(lr_values, len(layer_matches))
  
      def _get_select(f):
          def _select():
              return f(network).parameters()
  
          return _select
  
      def _get_filter(f):
          def _filter():
              # should eventually generate a list of network parameters
              return (
                  x[1] for x in filter(f, network.named_parameters())
              )
  
          return _filter
  
      params = []
      _layers = []
      for func, ty, lr in zip(layer_matches, match_types, lr_values):
          if ty.lower() == "select":
              layer_params = _get_select(func)
          elif ty.lower() == "filter":
              layer_params = _get_filter(func)
          else:
              raise ValueError(f"unsupported layer match type: {ty}.")
  
          params.append({"params": layer_params(), "lr": lr})
          _layers.extend([id(x) for x in layer_params()])
  
      if include_others:
          params.append(
              {
                  "params": filter(
                      lambda p: id(p) not in _layers,
                      network.parameters(),
                  )
              }
          )
  
      return params
  
imports: from __future__ import annotations
  from collections.abc import Callable, Sequence
  import torch
  from monai.utils import ensure_tuple, ensure_tuple_rep
label_desc: Use image node_id 1 for calling a global function with example usage: generate_param_groups(network, layer_matches, match_types, lr_values, include_others) and returns: params, _select, _filter, f, unknown