def METHOD_NAME():
    fake_channel_pruning_yaml = """
        model:
          name: imagenet_prune
          framework: pytorch
        pruning:
          approach:
            weight_compression_pytorch:
              initial_sparsity: 0.0
              target_sparsity: 0.9
              start_step: 0
              end_step: 10
              excluded_names: ["classifier"]
              update_frequency_on_step: 1
              sparsity_decay_type: "exp"
              pruners:
                - !Pruner
                    start_step: 5
                    end_step: 5
                    prune_type: "pattern_lock"
                    names: ['layer1.*']
                    extra_excluded_names: ['layer2.*']
                    prune_domain: "global"
                    pattern: "channelx1"
                - !Pruner
                    start_step: 1
                    end_step: 1
                    target_sparsity: 0.5
                    prune_type: "pattern_lock"
                    update_frequency: 2
                    names: ['layer2.*']
                    prune_domain: local
                    pattern: "2:4"
                - !Pruner
                    start_step: 2
                    end_step: 8
                    target_sparsity: 0.8
                    prune_type: "snip"
                    names: ['layer3.*']
                    prune_domain: "local"
                    pattern: "1xchannel"
                    sparsity_decay_type: "cube"
        """
    with open("fake_channel_pruning.yaml", "w", encoding="utf-8") as f:
        f.write(fake_channel_pruning_yaml)