How to use the smdebug.mxnet.SaveConfig function in smdebug

def test_save_config(hook=None):
    if hook is None:
        save_config_collection = SaveConfig(save_steps=[4, 5, 6])
        run_id = "trial_" + datetime.now().strftime("%Y%m%d-%H%M%S%f")
        out_dir = "/tmp/" + run_id
        save_config = SaveConfig(save_steps=[0, 1, 2, 3])
        hook = t_hook(
            out_dir=out_dir,
            save_config=save_config,
            include_collections=["ReluActivation", "weights", "biases", "gradients", "default"],
        )
        custom_collect = hook.get_collection("ReluActivation")
        custom_collect.save_config = save_config_collection
        custom_collect.include(["relu*", "input_*", "output*"])

    run_mnist_gluon_model(hook=hook, num_steps_train=10, num_steps_eval=10)
    if hook is None:
        shutil.rmtree(out_dir)

print("Registering the hook with out_dir {0}".format(out_dir))
        hook = t_hook(
            out_dir=out_dir,
            save_config=global_save_config,
            include_collections=[
                "weights",
                "biases",
                "gradients",
                "default",
                "ReluActivation",
                "flatten",
            ],
            reduction_config=global_reduce_config,
        )
        hook.get_collection("ReluActivation").include(["relu*"])
        hook.get_collection("ReluActivation").save_config = SaveConfig(save_steps=[4, 5, 6])
        hook.get_collection("ReluActivation").reduction_config = ReductionConfig(
            reductions=["min"], abs_reductions=["max"]
        )

        hook.get_collection("flatten").include(["flatten*"])
        hook.get_collection("flatten").save_config = SaveConfig(save_steps=[4, 5, 6])
        hook.get_collection("flatten").reduction_config = ReductionConfig(
            norms=["l1"], abs_norms=["l2"]
        )

    run_mnist_gluon_model(hook=hook, num_steps_train=10, num_steps_eval=10)

    # Testing
    print("Created the trial with out_dir {0}".format(out_dir))
    tr = create_trial(out_dir)
    assert tr

def test_save_all(hook=None, out_dir=None):
    hook_created = False
    if hook is None:
        hook_created = True
        save_config = SaveConfig(save_steps=[0, 1, 2, 3])
        run_id = "trial_" + datetime.now().strftime("%Y%m%d-%H%M%S%f")
        out_dir = "/tmp/" + run_id
        print("Registering the hook with out_dir {}".format(out_dir))
        hook = t_hook(out_dir=out_dir, save_config=save_config, save_all=True)
    run_mnist_gluon_model(hook=hook, num_steps_train=7, num_steps_eval=5)
    # assert for steps and tensor_names
    print("Created the trial with out_dir {}".format(out_dir))
    tr = create_trial(out_dir)
    tensor_list = tr.tensor_names()
    assert tr
    assert len(tr.steps()) == 4
    # some tensor names, like input and output, can't be retrieved from training session, so here we only assert for tensor numbers
    # 46 is gotten from index file
    # if no assertion failure, then the script could save all tensors
    assert len(tensor_list) == 46
    if hook_created:

def create_hook(output_s3_uri):
    # With the following SaveConfig, we will save tensors for steps 1, 2 and 3
    # (indexing starts with 0).
    save_config = SaveConfig(save_steps=[1, 2, 3])

    # Create a hook that logs weights, biases and gradients while training the model.
    hook = Hook(
        out_dir=output_s3_uri,
        save_config=save_config,
        include_collections=["weights", "gradients", "biases"],
    )
    return hook

def create_hook(output_s3_uri):
    save_config = SaveConfig(save_interval=1)
    custom_collect = smd.get_collection("ReluActivation")
    custom_collect.save_config = save_config
    custom_collect.include([".*relu_output"])
    hook = Hook(
        out_dir=output_s3_uri, save_config=save_config, include_collections=["ReluActivation"]
    )
    return hook

def test_save_config(hook=None, out_dir=None):
    hook_created = False
    if hook is None:
        hook_created = True
        global_reduce_config = ReductionConfig(reductions=["max", "mean"])
        global_save_config = SaveConfig(save_steps=[0, 1, 2, 3])

        run_id = "trial_" + datetime.now().strftime("%Y%m%d-%H%M%S%f")
        out_dir = "/tmp/newlogsRunTest/" + run_id
        print("Registering the hook with out_dir {0}".format(out_dir))
        hook = t_hook(
            out_dir=out_dir,
            save_config=global_save_config,
            include_collections=[
                "weights",
                "biases",
                "gradients",
                "default",
                "ReluActivation",
                "flatten",
            ],
            reduction_config=global_reduce_config,

def create_hook(output_s3_uri):
    save_config = SaveConfig(save_interval=1)
    custom_collect = smd.get_collection("inputData")
    custom_collect.save_config = save_config
    custom_collect.include([".*hybridsequential0_input_0"])
    hook = Hook(out_dir=output_s3_uri, save_config=save_config, include_collections=["inputData"])
    return hook

def create_hook(output_s3_uri):
    # With the following SaveConfig, we will save tensors for steps 0, 1, 2 and 3
    # (indexing starts with 0).
    save_config = SaveConfig(save_steps=[0, 1, 2, 3])
    # Create a hook that logs weights, biases and gradients while training the model.
    hook = Hook(
        out_dir=output_s3_uri,
        save_config=save_config,
        include_collections=["ReluActivation", "weights", "biases", "gradients"],
    )
    hook.get_collection("ReluActivation").include(["relu*", "input_*"])
    return hook