code: def check_and_identify_compresssion(args, ds_config):
      assert (
          args.per_device_train_batch_size
          == ds_config["train_micro_batch_size_per_gpu"]
      )
      assert (
          args.gradient_accumulation_steps
          == ds_config["train_batch_size"]
          / ds_config["train_micro_batch_size_per_gpu"]
      )
      quantization_enabled, prune_enabled, layer_reduction_enabled = (
          False,
          False,
          False,
      )
      if ds_config["compression_training"]["layer_reduction"][
          "enabled"
      ]:
          layer_reduction_enabled = True
  
      if (
          ds_config["compression_training"]["sparse_pruning"][
              "shared_parameters"
          ]["enabled"]
          or ds_config["compression_training"]["row_pruning"][
              "shared_parameters"
          ]["enabled"]
          or ds_config["compression_training"]["head_pruning"][
              "shared_parameters"
          ]["enabled"]
      ):
          prune_enabled = True
  
      if (
          ds_config["compression_training"]["weight_quantization"][
              "shared_parameters"
          ]["enabled"]
          or ds_config["compression_training"][
              "activation_quantization"
          ]["shared_parameters"]["enabled"]
      ):
          quantization_enabled = True
  
      return (
          layer_reduction_enabled,
          prune_enabled,
          quantization_enabled,
      )
  
imports: import torch
  from torch.nn import CrossEntropyLoss, MSELoss
  import datasets
  from datasets import load_dataset, load_metric
  import copy
  import os
  import json
  import transformers
  from transformers import AutoConfig, PretrainedConfig
  import huggingface_transformer
  from huggingface_transformer.modeling_bert import BertForSequenceClassification
  import logging
  import numpy
  import math
label_desc: Use image node_id 4 for calling a global function with example usage: check_and_identify_compresssion(args, ds_config) and returns: layer_reduction_enabled,  prune_enabled,  quantization_enabled