code: def assert_and_infer_cfg(cfg):
      # BN assertions.
      if cfg.BN.USE_PRECISE_STATS:
          assert cfg.BN.NUM_BATCHES_PRECISE >= 0
      # TRAIN assertions.
      assert cfg.TRAIN.CHECKPOINT_TYPE in ["pytorch", "caffe2"]
      assert (
          cfg.NUM_GPUS == 0 or cfg.TRAIN.BATCH_SIZE % cfg.NUM_GPUS == 0
      )
  
      # TEST assertions.
      assert cfg.TEST.CHECKPOINT_TYPE in ["pytorch", "caffe2"]
      assert (
          cfg.NUM_GPUS == 0 or cfg.TEST.BATCH_SIZE % cfg.NUM_GPUS == 0
      )
  
      # RESNET assertions.
      assert cfg.RESNET.NUM_GROUPS > 0
      assert cfg.RESNET.WIDTH_PER_GROUP > 0
      assert cfg.RESNET.WIDTH_PER_GROUP % cfg.RESNET.NUM_GROUPS == 0
  
      # Execute LR scaling by num_shards.
      if cfg.SOLVER.BASE_LR_SCALE_NUM_SHARDS:
          cfg.SOLVER.BASE_LR *= cfg.NUM_SHARDS
          cfg.SOLVER.WARMUP_START_LR *= cfg.NUM_SHARDS
          cfg.SOLVER.COSINE_END_LR *= cfg.NUM_SHARDS
  
      # General assertions.
      assert cfg.SHARD_ID < cfg.NUM_SHARDS
      return cfg
  
imports: import math
  from fvcore.common.config import CfgNode
  from .None import custom_config
label_desc: Use image node_id 1 for calling a global function with example usage: assert_and_infer_cfg(cfg) and returns: cfg