code: def load_from_state_dict(
      model,
      state_dict,
      strict: bool = True,
      apply_filter: bool = True,
      num_splits: int = 0,
  ):
      """Loads the model weights from the state dictionary."""
  
      # step 1: filter state dict
      if apply_filter:
          state_dict = _filter_state_dict(state_dict)
  
      state_dict = _fix_projection_head_keys(state_dict)
  
      # step 2: expand batchnorm weights
      state_dict = _maybe_expand_batchnorm_weights(
          model.state_dict(), state_dict, num_splits
      )
  
      # step 3: load from checkpoint
      model.load_state_dict(state_dict, strict=strict)
  
imports: import os
  import hydra
  import torch
  from hydra import utils
  from torch import nn
  from lightly.cli._cli_simclr import _SimCLR
  from lightly.embedding import SelfSupervisedEmbedding
  from lightly.models import ZOO
  from lightly.models import ResNetGenerator
  from lightly.models.batchnorm import get_norm_layer
  from lightly.utils.version_compare import version_compare
label_desc: Use image node_id 10 for calling a global function with example usage: load_from_state_dict(model, state_dict, strict, apply_filter, num_splits) without return types