[DOC] sort import of rst files

docs/Examples/HED.rst

@@ -25,16 +25,19 @@ model.
 
     # Import necessary libraries and modules
     import os.path as osp
+
+    import matplotlib.pyplot as plt
     import torch
     import torch.nn as nn
-    import matplotlib.pyplot as plt
-    from datasets import get_dataset, split_equation
-    from models.nn import SymbolNet
+
     from abl.learning import ABLModel, BasicNN
-    from reasoning import HedKB, HedReasoner
-    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log
+
     from bridge import HedBridge
+    from consistency_metric import ConsistencyMetric
+    from datasets import get_dataset, split_equation
+    from models.nn import SymbolNet
+    from reasoning import HedKB, HedReasoner
 
 Working with Data
 -----------------

docs/Examples/HWF.rst

@@ -22,17 +22,20 @@ machine learning model.
 
     # Import necessary libraries and modules
     import os.path as osp
+
+    import matplotlib.pyplot as plt
     import numpy as np
     import torch
     import torch.nn as nn
-    import matplotlib.pyplot as plt
-    from datasets import get_dataset
-    from models.nn import SymbolNet
+
+    from abl.bridge import SimpleBridge
+    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.learning import ABLModel, BasicNN
     from abl.reasoning import KBBase, Reasoner
-    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log
-    from abl.bridge import SimpleBridge
+
+    from datasets import get_dataset
+    from models.nn import SymbolNet
 
 Working with Data
 -----------------

docs/Examples/MNISTAdd.rst

@@ -21,19 +21,20 @@ machine learning model.
 
     # Import necessary libraries and modules
     import os.path as osp
+
+    import matplotlib.pyplot as plt
     import torch
     import torch.nn as nn
-    import matplotlib.pyplot as plt
-
     from torch.optim import RMSprop, lr_scheduler
 
-    from datasets import get_dataset
-    from models.nn import LeNet5
+    from abl.bridge import SimpleBridge
+    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.learning import ABLModel, BasicNN
     from abl.reasoning import KBBase, Reasoner
-    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log
-    from abl.bridge import SimpleBridge
+
+    from datasets import get_dataset
+    from models.nn import LeNet5
 
 Working with Data
 -----------------

docs/Examples/Zoo.rst

@@ -20,15 +20,18 @@ further update the learning model.
 
     # Import necessary libraries and modules
     import os.path as osp
+
     import numpy as np
     from sklearn.ensemble import RandomForestClassifier
-    from get_dataset import load_and_preprocess_dataset, split_dataset
+
+    from abl.bridge import SimpleBridge
+    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.learning import ABLModel
-    from kb import ZooKB
     from abl.reasoning import Reasoner
-    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-    from abl.utils import ABLLogger, print_log, confidence_dist
-    from abl.bridge import SimpleBridge
+    from abl.utils import ABLLogger, confidence_dist, print_log
+
+    from get_dataset import load_and_preprocess_dataset, split_dataset
+    from kb import ZooKB
 
 Working with Data
 -----------------

docs/Intro/Quick-Start.rst

@@ -21,12 +21,12 @@ In the MNIST Addition task, the data loading looks like
 
 .. code:: python
 
-   from datasets.get_mnist_add import get_mnist_add
+   from datasets import get_dataset
    
    # train_data and test_data are tuples in the format (X, gt_pseudo_label, Y)
    # If get_pseudo_label is set to False, the gt_pseudo_label in each tuple will be None.
-   train_data = get_mnist_add(train=True, get_pseudo_label=True)
-   test_data = get_mnist_add(train=False, get_pseudo_label=True)
+   train_data = get_dataset(train=True, get_pseudo_label=True)
+   test_data = get_dataset(train=False, get_pseudo_label=True)
 
 Read more about `preparing datasets <Datasets.html>`_.