diff --git a/explaining_framework/config/explainer_config/eixgnn_config.py b/explaining_framework/config/explainer_config/eixgnn_config.py
index d5106d8..0bd57b7 100644
--- a/explaining_framework/config/explainer_config/eixgnn_config.py
+++ b/explaining_framework/config/explainer_config/eixgnn_config.py
@@ -36,267 +36,125 @@ def set_eixgnn_cfg(eixgnn_cfg):
     if eixgnn_cfg is None:
         return eixgnn_cfg
 
-    # ----------------------------------------------------------------------- #
-    # Basic options
-    # ----------------------------------------------------------------------- #
-
-    # Set print destination: stdout / file / both
-    eixgnn_cfg.print = "both"
-
-    eixgnn_cfg.out_dir = "./explanations"
-
-    eixgnn_cfg.cfg_dest = "explaining_config.yaml"
-
     eixgnn_cfg.seed = 0
-
-    # ----------------------------------------------------------------------- #
-    # Dataset options
-    # ----------------------------------------------------------------------- #
-
-    eixgnn_cfg.dataset = CN()
-
-    eixgnn_cfg.dataset.name = "Cora"
-
-    eixgnn_cfg.run_topological_stat = True
-
-    # ----------------------------------------------------------------------- #
-    # Model options
-    # ----------------------------------------------------------------------- #
-
-    eixgnn_cfg.model = CN()
-
-    # Set wether or not load the best model for given dataset or a path
-    eixgnn_cfg.model.ckpt = "best"
-
-    # ----------------------------------------------------------------------- #
-    # Explainer options
-    # ----------------------------------------------------------------------- #
-
-    eixgnn_cfg.explainer = CN()
-
-    # Name of the explaining method
-    eixgnn_cfg.explainer.name = "EiXGNN"
-
-    # Whether or not to provide specific explaining methods configuration or default configuration
-    eixgnn_cfg.explainer.cfg = "default"
-
-    # ----------------------------------------------------------------------- #
-    # Explaining options
-    # ----------------------------------------------------------------------- #
-
-    # 'ExplanationType : 'model' or 'phenomenon'
-    eixgnn_cfg.explanation_type = "model"
-
-    eixgnn_cfg.model_config = CN()
-
-    # Do not modify it, will be handled by dataset , assuming one dataset = one learning task
-    eixgnn_cfg.model_config.mode = None
-
-    # Do not modify it, will be handled by dataset , assuming one dataset = one learning task
-    eixgnn_cfg.model_config.task_level = None
-
-    # Do not modify it, we always assume here that model output are 'raw'
-    eixgnn_cfg.model_config.return_type = "raw"
-
-    eixgnn_cfg.threshold_config = CN()
-
-    eixgnn_cfg.threshold_config.threshold_type = None
-
-    eixgnn_cfg.threshold_config.value = 0.5
-
-    # Set print destination: stdout / file / both
-    eixgnn_cfg.print = "both"
-
-    # Select device: 'cpu', 'cuda', 'auto'
-    eixgnn_cfg.accelerator = "auto"
-
-    # Output directory
-    eixgnn_cfg.out_dir = "results"
-
-    # Config name (in out_dir)
-    eixgnn_cfg.eixgnn_cfg_dest = "config.yaml"
-
-    # Random seed
-    eixgnn_cfg.seed = 0
-    # ----------------------------------------------------------------------- #
-    # Globally shared variables:
-    # These variables will be set dynamically based on the input dataset
-    # Do not directly set them here or in .yaml files
-    # ----------------------------------------------------------------------- #
-
-    eixgnn_cfg.share = CN()
-
-    # Size of input dimension
-    eixgnn_cfg.share.dim_in = 1
-
-    # Size of out dimension, i.e., number of labels to be predicted
-    eixgnn_cfg.share.dim_out = 1
-
-    # Number of dataset splits: train/val/test
-    eixgnn_cfg.share.num_splits = 1
-
-    # ----------------------------------------------------------------------- #
-    # Dataset options
-    # ----------------------------------------------------------------------- #
-    eixgnn_cfg.dataset = CN()
-
-    # Name of the dataset
-    eixgnn_cfg.dataset.name = "Cora"
-
-    # if PyG: look for it in Pytorch Geometric dataset
-    # if NetworkX/nx: load data in NetworkX format
-
-    # Dir to load the dataset. If the dataset is downloaded, this is the
-    # cache dir
-    eixgnn_cfg.dataset.dir = "./datasets"
-    # ----------------------------------------------------------------------- #
-    # Memory options
-    # ----------------------------------------------------------------------- #
-    eixgnn_cfg.mem = CN()
-
-    # Perform ReLU inplace
-    eixgnn_cfg.mem.inplace = False
-
-    # Set user customized eixgnn_cfgs
-    for func in register.config_dict.values():
-        func(eixgnn_cfg)
+    eixgnn_cfg.L = 50
+    eixgnn_cfg.p = 0.5
+    eixgnn_cfg.importance_sampling_strategy = "node"
+    eixgnn_cfg.domain_similarity = "relative_edge_density"
+    eixgnn_cfg.signal_similarity = "KL"
+    eixgnn_cfg.shapley_value_approx = 100
 
 
 def assert_eixgnn_cfg(eixgnn_cfg):
     r"""Checks config values, do necessary post processing to the configs"""
-    if eixgnn_cfg.dataset.task not in ["node", "edge", "graph", "link_pred"]:
-        raise ValueError(
-            "Task {} not supported, must be one of node, "
-            "edge, graph, link_pred".format(eixgnn_cfg.dataset.task)
-        )
-    if (
-        "classification" in eixgnn_cfg.dataset.task_type
-        and eixgnn_cfg.model.loss_fun == "mse"
-    ):
-        eixgnn_cfg.model.loss_fun = "cross_entropy"
-        logging.warning("model.loss_fun changed to cross_entropy for classification.")
-    if (
-        eixgnn_cfg.dataset.task_type == "regression"
-        and eixgnn_cfg.model.loss_fun == "cross_entropy"
-    ):
-        eixgnn_cfg.model.loss_fun = "mse"
-        logging.warning("model.loss_fun changed to mse for regression.")
-    if eixgnn_cfg.dataset.task == "graph" and eixgnn_cfg.dataset.transductive:
-        eixgnn_cfg.dataset.transductive = False
-        logging.warning("dataset.transductive changed " "to False for graph task.")
-    if eixgnn_cfg.gnn.layers_post_mp < 1:
-        eixgnn_cfg.gnn.layers_post_mp = 1
-        logging.warning("Layers after message passing should be >=1")
-    if eixgnn_cfg.gnn.head == "default":
-        eixgnn_cfg.gnn.head = eixgnn_cfg.dataset.task
-    eixgnn_cfg.run_dir = eixgnn_cfg.out_dir
-
-
-def dump_eixgnn_cfg(eixgnn_cfg):
-    r"""
-    Dumps the config to the output directory specified in
-    :obj:`eixgnn_cfg.out_dir`
-    Args:
-        eixgnn_cfg (CfgNode): Configuration node
-    """
-    makedirs(eixgnn_cfg.out_dir)
-    eixgnn_cfg_file = os.path.join(eixgnn_cfg.out_dir, eixgnn_cfg.eixgnn_cfg_dest)
-    with open(eixgnn_cfg_file, "w") as f:
-        eixgnn_cfg.dump(stream=f)
-
-
-def load_eixgnn_cfg(eixgnn_cfg, args):
-    r"""
-    Load configurations from file system and command line
-    Args:
-        eixgnn_cfg (CfgNode): Configuration node
-        args (ArgumentParser): Command argument parser
-    """
-    eixgnn_cfg.merge_from_file(args.eixgnn_cfg_file)
-    eixgnn_cfg.merge_from_list(args.opts)
-    assert_eixgnn_cfg(eixgnn_cfg)
-
-
-def makedirs_rm_exist(dir):
-    if os.path.isdir(dir):
-        shutil.rmtree(dir)
-    os.makedirs(dir, exist_ok=True)
-
-
-def get_fname(fname):
-    r"""
-    Extract filename from file name path
-    Args:
-        fname (string): Filename for the yaml format configuration file
-    """
-    fname = fname.split("/")[-1]
-    if fname.endswith(".yaml"):
-        fname = fname[:-5]
-    elif fname.endswith(".yml"):
-        fname = fname[:-4]
-    return fname
-
-
-def set_out_dir(out_dir, fname):
-    r"""
-    Create the directory for full experiment run
-    Args:
-        out_dir (string): Directory for output, specified in :obj:`eixgnn_cfg.out_dir`
-        fname (string): Filename for the yaml format configuration file
-    """
-    fname = get_fname(fname)
-    eixgnn_cfg.out_dir = os.path.join(out_dir, fname)
-    # Make output directory
-    if eixgnn_cfg.train.auto_resume:
-        os.makedirs(eixgnn_cfg.out_dir, exist_ok=True)
-    else:
-        makedirs_rm_exist(eixgnn_cfg.out_dir)
-
-
-def set_run_dir(out_dir):
-    r"""
-    Create the directory for each random seed experiment run
-    Args:
-        out_dir (string): Directory for output, specified in :obj:`eixgnn_cfg.out_dir`
-        fname (string): Filename for the yaml format configuration file
-    """
-    eixgnn_cfg.run_dir = os.path.join(out_dir, str(eixgnn_cfg.seed))
-    # Make output directory
-    if eixgnn_cfg.train.auto_resume:
-        os.makedirs(eixgnn_cfg.run_dir, exist_ok=True)
-    else:
-        makedirs_rm_exist(eixgnn_cfg.run_dir)
+    if not (0 <= eixgnn_cfg.p and eixgnn_cfg.p <= 1):
+        raise ValueError("p needs to be between 0 and 1")
 
 
 set_eixgnn_cfg(eixgnn_cfg)
 
-
-def from_config(func):
-    if inspect.isclass(func):
-        params = list(inspect.signature(func.__init__).parameters.values())[1:]
-    else:
-        params = list(inspect.signature(func).parameters.values())
-
-    arg_names = [p.name for p in params]
-    has_defaults = [p.default != inspect.Parameter.empty for p in params]
-
-    @functools.wraps(func)
-    def wrapper(*args, eixgnn_cfg: Any = None, **kwargs):
-        if eixgnn_cfg is not None:
-            eixgnn_cfg = (
-                dict(eixgnn_cfg)
-                if isinstance(eixgnn_cfg, Iterable)
-                else asdict(eixgnn_cfg)
-            )
-
-            iterator = zip(arg_names[len(args) :], has_defaults[len(args) :])
-            for arg_name, has_default in iterator:
-                if arg_name in kwargs:
-                    continue
-                elif arg_name in eixgnn_cfg:
-                    kwargs[arg_name] = eixgnn_cfg[arg_name]
-                elif not has_default:
-                    raise ValueError(f"'eixgnn_cfg.{arg_name}' undefined")
-        return func(*args, **kwargs)
-
-    return wrapper
+#  def dump_eixgnn_cfg(eixgnn_cfg,path):
+#  r"""
+#  Dumps the config to the output directory specified in
+#  :obj:`eixgnn_cfg.out_dir`
+#  Args:
+#  eixgnn_cfg (CfgNode): Configuration node
+#  """
+#  makedirs(eixgnn_cfg.out_dir)
+#  eixgnn_cfg_file = os.path.join(eixgnn_cfg.out_dir, eixgnn_cfg.eixgnn_cfg_dest)
+#  with open(eixgnn_cfg_file, "w") as f:
+#  eixgnn_cfg.dump(stream=f)
+#
+#
+#  def load_eixgnn_cfg(eixgnn_cfg, args):
+#  r"""
+#  Load configurations from file system and command line
+#  Args:
+#  eixgnn_cfg (CfgNode): Configuration node
+#  args (ArgumentParser): Command argument parser
+#  """
+#  eixgnn_cfg.merge_from_file(args.eixgnn_cfg_file)
+#  eixgnn_cfg.merge_from_list(args.opts)
+#  assert_eixgnn_cfg(eixgnn_cfg)
+#
+#
+#  def makedirs_rm_exist(dir):
+#  if os.path.isdir(dir):
+#  shutil.rmtree(dir)
+#  os.makedirs(dir, exist_ok=True)
+#
+#
+#  def get_fname(fname):
+#  r"""
+#  Extract filename from file name path
+#  Args:
+#  fname (string): Filename for the yaml format configuration file
+#  """
+#  fname = fname.split("/")[-1]
+#  if fname.endswith(".yaml"):
+#  fname = fname[:-5]
+#  elif fname.endswith(".yml"):
+#  fname = fname[:-4]
+#  return fname
+#
+#
+#  def set_out_dir(out_dir, fname):
+#  r"""
+#  Create the directory for full experiment run
+#  Args:
+#  out_dir (string): Directory for output, specified in :obj:`eixgnn_cfg.out_dir`
+#  fname (string): Filename for the yaml format configuration file
+#  """
+#  fname = get_fname(fname)
+#  eixgnn_cfg.out_dir = os.path.join(out_dir, fname)
+#  Make output directory
+#  if eixgnn_cfg.train.auto_resume:
+#  os.makedirs(eixgnn_cfg.out_dir, exist_ok=True)
+#  else:
+#  makedirs_rm_exist(eixgnn_cfg.out_dir)
+#
+#
+#  def set_run_dir(out_dir):
+#  r"""
+#  Create the directory for each random seed experiment run
+#  Args:
+#  out_dir (string): Directory for output, specified in :obj:`eixgnn_cfg.out_dir`
+#  fname (string): Filename for the yaml format configuration file
+#  """
+#  eixgnn_cfg.run_dir = os.path.join(out_dir, str(eixgnn_cfg.seed))
+#  Make output directory
+#  if eixgnn_cfg.train.auto_resume:
+#  os.makedirs(eixgnn_cfg.run_dir, exist_ok=True)
+#  else:
+#  makedirs_rm_exist(eixgnn_cfg.run_dir)
+#
+#
+#  def from_config(func):
+#  if inspect.isclass(func):
+#  params = list(inspect.signature(func.__init__).parameters.values())[1:]
+#  else:
+#  params = list(inspect.signature(func).parameters.values())
+#
+#  arg_names = [p.name for p in params]
+#  has_defaults = [p.default != inspect.Parameter.empty for p in params]
+#
+#  @functools.wraps(func)
+#  def wrapper(*args, eixgnn_cfg: Any = None, **kwargs):
+#  if eixgnn_cfg is not None:
+#  eixgnn_cfg = (
+#  dict(eixgnn_cfg)
+#  if isinstance(eixgnn_cfg, Iterable)
+#  else asdict(eixgnn_cfg)
+#  )
+#
+#  iterator = zip(arg_names[len(args) :], has_defaults[len(args) :])
+#  for arg_name, has_default in iterator:
+#  if arg_name in kwargs:
+#  continue
+#  elif arg_name in eixgnn_cfg:
+#  kwargs[arg_name] = eixgnn_cfg[arg_name]
+#  elif not has_default:
+#  raise ValueError(f"'eixgnn_cfg.{arg_name}' undefined")
+#  return func(*args, **kwargs)
+#
+#  return wrapper
diff --git a/explaining_framework/config/explainer_config/scgnn_config.py b/explaining_framework/config/explainer_config/scgnn_config.py
index 660b1ea..adbb741 100644
--- a/explaining_framework/config/explainer_config/scgnn_config.py
+++ b/explaining_framework/config/explainer_config/scgnn_config.py
@@ -14,9 +14,9 @@ from torch_geometric.data.makedirs import makedirs
 try:  # Define global config object
     from yacs.config import CfgNode as CN
 
-    explaining_cfg = CN()
+    scgnn_cfg = CN()
 except ImportError:
-    explaining_cfg = None
+    scgnn_cfg = None
     warnings.warn(
         "Could not define global config object. Please install "
         "'yacs' for using the GraphGym experiment manager via "
@@ -24,199 +24,64 @@ except ImportError:
     )
 
 
-def set_explaining_cfg(explaining_cfg):
+def set_scgnn_cfg(scgnn_cfg):
     r"""
     This function sets the default config value.
     1) Note that for an experiment, only part of the arguments will be used
     The remaining unused arguments won't affect anything.
     So feel free to register any argument in graphgym.contrib.config
-    2) We support *at most* two levels of configs, e.g., explaining_cfg.dataset.name
+    2) We support *at most* two levels of configs, e.g., scgnn_cfg.dataset.name
     :return: configuration use by the experiment.
     """
-    if explaining_cfg is None:
-        return explaining_cfg
+    if scgnn_cfg is None:
+        return scgnn_cfg
 
-    # ----------------------------------------------------------------------- #
-    # Basic options
-    # ----------------------------------------------------------------------- #
-
-    # Set print destination: stdout / file / both
-    explaining_cfg.print = "both"
-
-    explaining_cfg.out_dir = "./explanations"
-
-    explaining_cfg.cfg_dest = "explaining_config.yaml"
-
-    explaining_cfg.seed = 0
-
-    # ----------------------------------------------------------------------- #
-    # Dataset options
-    # ----------------------------------------------------------------------- #
-
-    explaining_cfg.dataset = CN()
-
-    explaining_cfg.dataset.name = "Cora"
-
-    explaining_cfg.run_topological_stat = True
-
-    # ----------------------------------------------------------------------- #
-    # Model options
-    # ----------------------------------------------------------------------- #
-
-    explaining_cfg.model = CN()
-
-    # Set wether or not load the best model for given dataset or a path
-    explaining_cfg.model.ckpt = "best"
-
-    # ----------------------------------------------------------------------- #
-    # Explainer options
-    # ----------------------------------------------------------------------- #
-
-    explaining_cfg.explainer = CN()
-
-    # Name of the explaining method
-    explaining_cfg.explainer.name = "EiXGNN"
-
-    # Whether or not to provide specific explaining methods configuration or default configuration
-    explaining_cfg.explainer.cfg = "default"
-
-    # ----------------------------------------------------------------------- #
-    # Explaining options
-    # ----------------------------------------------------------------------- #
-
-    # 'ExplanationType : 'model' or 'phenomenon'
-    explaining_cfg.explanation_type = "model"
-
-    explaining_cfg.model_config = CN()
-
-    # Do not modify it, will be handled by dataset , assuming one dataset = one learning task
-    explaining_cfg.model_config.mode = None
-
-    # Do not modify it, will be handled by dataset , assuming one dataset = one learning task
-    explaining_cfg.model_config.task_level = None
-
-    # Do not modify it, we always assume here that model output are 'raw'
-    explaining_cfg.model_config.return_type = "raw"
-
-    explaining_cfg.threshold_config = CN()
-
-    explaining_cfg.threshold_config.threshold_type = None
-
-    explaining_cfg.threshold_config.value = 0.5
-
-    # Set print destination: stdout / file / both
-    explaining_cfg.print = "both"
-
-    # Select device: 'cpu', 'cuda', 'auto'
-    explaining_cfg.accelerator = "auto"
-
-    # Output directory
-    explaining_cfg.out_dir = "results"
-
-    # Config name (in out_dir)
-    explaining_cfg.explaining_cfg_dest = "config.yaml"
-
-    # Random seed
-    explaining_cfg.seed = 0
-    # ----------------------------------------------------------------------- #
-    # Globally shared variables:
-    # These variables will be set dynamically based on the input dataset
-    # Do not directly set them here or in .yaml files
-    # ----------------------------------------------------------------------- #
-
-    explaining_cfg.share = CN()
-
-    # Size of input dimension
-    explaining_cfg.share.dim_in = 1
-
-    # Size of out dimension, i.e., number of labels to be predicted
-    explaining_cfg.share.dim_out = 1
-
-    # Number of dataset splits: train/val/test
-    explaining_cfg.share.num_splits = 1
-
-    # ----------------------------------------------------------------------- #
-    # Dataset options
-    # ----------------------------------------------------------------------- #
-    explaining_cfg.dataset = CN()
-
-    # Name of the dataset
-    explaining_cfg.dataset.name = "Cora"
-
-    # if PyG: look for it in Pytorch Geometric dataset
-    # if NetworkX/nx: load data in NetworkX format
-
-    # Dir to load the dataset. If the dataset is downloaded, this is the
-    # cache dir
-    explaining_cfg.dataset.dir = "./datasets"
-    # ----------------------------------------------------------------------- #
-    # Memory options
-    # ----------------------------------------------------------------------- #
-    explaining_cfg.mem = CN()
-
-    # Perform ReLU inplace
-    explaining_cfg.mem.inplace = False
-
-    # Set user customized explaining_cfgs
-    for func in register.config_dict.values():
-        func(explaining_cfg)
+    scgnn_cfg.depth = 'all'
+    scgnn_cfg.interest_map_norm = True
+    scgnn_cfg.score_map_norm = True
 
 
-def assert_explaining_cfg(explaining_cfg):
-    r"""Checks config values, do necessary post processing to the configs"""
-    if explaining_cfg.dataset.task not in ["node", "edge", "graph", "link_pred"]:
+
+
+def assert_scgnn_cfg(scgnn_cfg):
+    r"""Checks config values, do necessary post processing to the configs
+        TODO
+
+        """
+    if scgnn_cfg. not in ["node", "edge", "graph", "link_pred"]:
         raise ValueError(
             "Task {} not supported, must be one of node, "
-            "edge, graph, link_pred".format(explaining_cfg.dataset.task)
+            "edge, graph, link_pred".format(scgnn_cfg.dataset.task)
         )
-    if (
-        "classification" in explaining_cfg.dataset.task_type
-        and explaining_cfg.model.loss_fun == "mse"
-    ):
-        explaining_cfg.model.loss_fun = "cross_entropy"
-        logging.warning("model.loss_fun changed to cross_entropy for classification.")
-    if (
-        explaining_cfg.dataset.task_type == "regression"
-        and explaining_cfg.model.loss_fun == "cross_entropy"
-    ):
-        explaining_cfg.model.loss_fun = "mse"
-        logging.warning("model.loss_fun changed to mse for regression.")
-    if explaining_cfg.dataset.task == "graph" and explaining_cfg.dataset.transductive:
-        explaining_cfg.dataset.transductive = False
-        logging.warning("dataset.transductive changed " "to False for graph task.")
-    if explaining_cfg.gnn.layers_post_mp < 1:
-        explaining_cfg.gnn.layers_post_mp = 1
-        logging.warning("Layers after message passing should be >=1")
-    if explaining_cfg.gnn.head == "default":
-        explaining_cfg.gnn.head = explaining_cfg.dataset.task
-    explaining_cfg.run_dir = explaining_cfg.out_dir
+        scgnn_cfg.run_dir = scgnn_cfg.out_dir
 
 
-def dump_explaining_cfg(explaining_cfg):
+def dump_scgnn_cfg(scgnn_cfg,path):
     r"""
+    TODO
     Dumps the config to the output directory specified in
-    :obj:`explaining_cfg.out_dir`
+    :obj:`scgnn_cfg.out_dir`
     Args:
-        explaining_cfg (CfgNode): Configuration node
+        scgnn_cfg (CfgNode): Configuration node
     """
-    makedirs(explaining_cfg.out_dir)
-    explaining_cfg_file = os.path.join(
-        explaining_cfg.out_dir, explaining_cfg.explaining_cfg_dest
+    makedirs(scgnn_cfg.out_dir)
+    scgnn_cfg_file = os.path.join(
+        scgnn_cfg.out_dir, scgnn_cfg.scgnn_cfg_dest
     )
-    with open(explaining_cfg_file, "w") as f:
-        explaining_cfg.dump(stream=f)
+    with open(scgnn_cfg_file, "w") as f:
+        scgnn_cfg.dump(stream=f)
 
 
-def load_explaining_cfg(explaining_cfg, args):
+def load_scgnn_cfg(scgnn_cfg, args):
     r"""
     Load configurations from file system and command line
     Args:
-        explaining_cfg (CfgNode): Configuration node
+        scgnn_cfg (CfgNode): Configuration node
         args (ArgumentParser): Command argument parser
     """
-    explaining_cfg.merge_from_file(args.explaining_cfg_file)
-    explaining_cfg.merge_from_list(args.opts)
-    assert_explaining_cfg(explaining_cfg)
+    scgnn_cfg.merge_from_file(args.scgnn_cfg_file)
+    scgnn_cfg.merge_from_list(args.opts)
+    assert_scgnn_cfg(scgnn_cfg)
 
 
 def makedirs_rm_exist(dir):
@@ -243,34 +108,34 @@ def set_out_dir(out_dir, fname):
     r"""
     Create the directory for full experiment run
     Args:
-        out_dir (string): Directory for output, specified in :obj:`explaining_cfg.out_dir`
+        out_dir (string): Directory for output, specified in :obj:`scgnn_cfg.out_dir`
         fname (string): Filename for the yaml format configuration file
     """
     fname = get_fname(fname)
-    explaining_cfg.out_dir = os.path.join(out_dir, fname)
+    scgnn_cfg.out_dir = os.path.join(out_dir, fname)
     # Make output directory
-    if explaining_cfg.train.auto_resume:
-        os.makedirs(explaining_cfg.out_dir, exist_ok=True)
+    if scgnn_cfg.train.auto_resume:
+        os.makedirs(scgnn_cfg.out_dir, exist_ok=True)
     else:
-        makedirs_rm_exist(explaining_cfg.out_dir)
+        makedirs_rm_exist(scgnn_cfg.out_dir)
 
 
 def set_run_dir(out_dir):
     r"""
     Create the directory for each random seed experiment run
     Args:
-        out_dir (string): Directory for output, specified in :obj:`explaining_cfg.out_dir`
+        out_dir (string): Directory for output, specified in :obj:`scgnn_cfg.out_dir`
         fname (string): Filename for the yaml format configuration file
     """
-    explaining_cfg.run_dir = os.path.join(out_dir, str(explaining_cfg.seed))
+    scgnn_cfg.run_dir = os.path.join(out_dir, str(scgnn_cfg.seed))
     # Make output directory
-    if explaining_cfg.train.auto_resume:
-        os.makedirs(explaining_cfg.run_dir, exist_ok=True)
+    if scgnn_cfg.train.auto_resume:
+        os.makedirs(scgnn_cfg.run_dir, exist_ok=True)
     else:
-        makedirs_rm_exist(explaining_cfg.run_dir)
+        makedirs_rm_exist(scgnn_cfg.run_dir)
 
 
-set_explaining_cfg(explaining_cfg)
+set_scgnn_cfg(scgnn_cfg)
 
 
 def from_config(func):
@@ -283,22 +148,22 @@ def from_config(func):
     has_defaults = [p.default != inspect.Parameter.empty for p in params]
 
     @functools.wraps(func)
-    def wrapper(*args, explaining_cfg: Any = None, **kwargs):
-        if explaining_cfg is not None:
-            explaining_cfg = (
-                dict(explaining_cfg)
-                if isinstance(explaining_cfg, Iterable)
-                else asdict(explaining_cfg)
+    def wrapper(*args, scgnn_cfg: Any = None, **kwargs):
+        if scgnn_cfg is not None:
+            scgnn_cfg = (
+                dict(scgnn_cfg)
+                if isinstance(scgnn_cfg, Iterable)
+                else asdict(scgnn_cfg)
             )
 
             iterator = zip(arg_names[len(args) :], has_defaults[len(args) :])
             for arg_name, has_default in iterator:
                 if arg_name in kwargs:
                     continue
-                elif arg_name in explaining_cfg:
-                    kwargs[arg_name] = explaining_cfg[arg_name]
+                elif arg_name in scgnn_cfg:
+                    kwargs[arg_name] = scgnn_cfg[arg_name]
                 elif not has_default:
-                    raise ValueError(f"'explaining_cfg.{arg_name}' undefined")
+                    raise ValueError(f"'scgnn_cfg.{arg_name}' undefined")
         return func(*args, **kwargs)
 
     return wrapper
diff --git a/explaining_framework/config/explaining_config.py b/explaining_framework/config/explaining_config.py
index 660b1ea..96c271b 100644
--- a/explaining_framework/config/explaining_config.py
+++ b/explaining_framework/config/explaining_config.py
@@ -24,7 +24,7 @@ except ImportError:
     )
 
 
-def set_explaining_cfg(explaining_cfg):
+def set_cfg(explaining_cfg):
     r"""
     This function sets the default config value.
     1) Note that for an experiment, only part of the arguments will be used
@@ -110,89 +110,23 @@ def set_explaining_cfg(explaining_cfg):
     # Select device: 'cpu', 'cuda', 'auto'
     explaining_cfg.accelerator = "auto"
 
-    # Output directory
-    explaining_cfg.out_dir = "results"
-
     # Config name (in out_dir)
     explaining_cfg.explaining_cfg_dest = "config.yaml"
 
-    # Random seed
     explaining_cfg.seed = 0
-    # ----------------------------------------------------------------------- #
-    # Globally shared variables:
-    # These variables will be set dynamically based on the input dataset
-    # Do not directly set them here or in .yaml files
-    # ----------------------------------------------------------------------- #
 
-    explaining_cfg.share = CN()
-
-    # Size of input dimension
-    explaining_cfg.share.dim_in = 1
-
-    # Size of out dimension, i.e., number of labels to be predicted
-    explaining_cfg.share.dim_out = 1
-
-    # Number of dataset splits: train/val/test
-    explaining_cfg.share.num_splits = 1
-
-    # ----------------------------------------------------------------------- #
-    # Dataset options
-    # ----------------------------------------------------------------------- #
-    explaining_cfg.dataset = CN()
-
-    # Name of the dataset
-    explaining_cfg.dataset.name = "Cora"
-
-    # if PyG: look for it in Pytorch Geometric dataset
-    # if NetworkX/nx: load data in NetworkX format
-
-    # Dir to load the dataset. If the dataset is downloaded, this is the
-    # cache dir
     explaining_cfg.dataset.dir = "./datasets"
-    # ----------------------------------------------------------------------- #
-    # Memory options
-    # ----------------------------------------------------------------------- #
-    explaining_cfg.mem = CN()
 
-    # Perform ReLU inplace
-    explaining_cfg.mem.inplace = False
-
-    # Set user customized explaining_cfgs
-    for func in register.config_dict.values():
-        func(explaining_cfg)
+    explaining_cfg.relu_and_normalize = True
 
 
-def assert_explaining_cfg(explaining_cfg):
+
+def assert_cfg(explaining_cfg):
     r"""Checks config values, do necessary post processing to the configs"""
-    if explaining_cfg.dataset.task not in ["node", "edge", "graph", "link_pred"]:
-        raise ValueError(
-            "Task {} not supported, must be one of node, "
-            "edge, graph, link_pred".format(explaining_cfg.dataset.task)
-        )
-    if (
-        "classification" in explaining_cfg.dataset.task_type
-        and explaining_cfg.model.loss_fun == "mse"
-    ):
-        explaining_cfg.model.loss_fun = "cross_entropy"
-        logging.warning("model.loss_fun changed to cross_entropy for classification.")
-    if (
-        explaining_cfg.dataset.task_type == "regression"
-        and explaining_cfg.model.loss_fun == "cross_entropy"
-    ):
-        explaining_cfg.model.loss_fun = "mse"
-        logging.warning("model.loss_fun changed to mse for regression.")
-    if explaining_cfg.dataset.task == "graph" and explaining_cfg.dataset.transductive:
-        explaining_cfg.dataset.transductive = False
-        logging.warning("dataset.transductive changed " "to False for graph task.")
-    if explaining_cfg.gnn.layers_post_mp < 1:
-        explaining_cfg.gnn.layers_post_mp = 1
-        logging.warning("Layers after message passing should be >=1")
-    if explaining_cfg.gnn.head == "default":
-        explaining_cfg.gnn.head = explaining_cfg.dataset.task
     explaining_cfg.run_dir = explaining_cfg.out_dir
 
 
-def dump_explaining_cfg(explaining_cfg):
+def dump_cfg(explaining_cfg):
     r"""
     Dumps the config to the output directory specified in
     :obj:`explaining_cfg.out_dir`
@@ -207,7 +141,7 @@ def dump_explaining_cfg(explaining_cfg):
         explaining_cfg.dump(stream=f)
 
 
-def load_explaining_cfg(explaining_cfg, args):
+def load_cfg(explaining_cfg, args):
     r"""
     Load configurations from file system and command line
     Args:
@@ -270,7 +204,7 @@ def set_run_dir(out_dir):
         makedirs_rm_exist(explaining_cfg.run_dir)
 
 
-set_explaining_cfg(explaining_cfg)
+set_cfg(explaining_cfg)
 
 
 def from_config(func):
diff --git a/explaining_framework/utils/explaining/load_model.py b/explaining_framework/utils/explaining/load_model.py
new file mode 100644
index 0000000..8f506c9
--- /dev/null
+++ b/explaining_framework/utils/explaining/load_model.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import logging
+import os
+import json
+import glob
+
+import torch
+from torch_geometric.graphgym.model_builder import create_model
+from torch_geometric.graphgym.train import GraphGymDataModule
+
+MODEL_STATE = "model_state"
+OPTIMIZER_STATE = "optimizer_state"
+SCHEDULER_STATE = "scheduler_state"
+
+
+def load_ckpt(
+    model: torch.nn.Module,
+    ckpt_path: str,
+) -> torch.nn.Module:
+    r"""Loads the model at given checkpoint."""
+
+    if not osp.exists(path):
+        return None
+
+    ckpt = torch.load(ckpt_path)
+    model.load_state_dict(ckpt[MODEL_STATE])
+
+    return model
+
+
+def load_best_given_exp(path_to_xp:str, wrt_metric:str:'val')->str:
+    path = os.path.normpath(path)
+    path.split(os.sep)
+    for path in glob.glob(os.path.join(path_to_xp,'[0-9]'*10,wrt_metric,'stats.json')):
+        print(path)
+
+
+
+
diff --git a/explaining_framework/utils/explanation/io.py b/explaining_framework/utils/explanation/io.py
index 7c56227..9af1a57 100644
--- a/explaining_framework/utils/explanation/io.py
+++ b/explaining_framework/utils/explanation/io.py
@@ -43,6 +43,7 @@ def load_explanation(path: str) -> Explanation:
 
 
 def normalize_explanation_masks(exp: Explanation, p: str = "inf") -> Explanation:
+    exp = copy.copy(exp)
     data = exp.to_dict()
     for k, v in data.items():
         if "_mask" in k and isinstance(v, torch.FloatTensor):