fix the examples in docs of minddata

mindspore/dataset/core/config.py

@@ -340,8 +340,7 @@ def load(file):
         RuntimeError: If file is invalid and parsing fails.
 
     Examples:
-        >>> # Set new default configuration values according to values in the configuration file.
+        >>> # Set new default configuration according to values in the configuration file.
-        >>> ds.config.load("/path/to/config_directory/config.cfg")
         >>> # example config file:
         >>> # {
         >>> #     "logFilePath": "/tmp",
@@ -349,5 +348,7 @@ def load(file):
         >>> #     "seed": 5489,
         >>> #     "monitorSamplingInterval": 30
         >>> # }
+        >>> config_file = "/path/to/config/file"
+        >>> ds.config.load(config_file)
     """
     _config.load(file)

mindspore/dataset/engine/graphdata.py

@@ -73,7 +73,7 @@ class GraphData:
 
     Examples:
         >>> graph_dataset = ds.GraphData(graph_dataset_dir, 2)
-        >>> nodes = graph_dataset.get_all_nodes(0)
+        >>> nodes = graph_dataset.get_all_nodes(1)
         >>> features = graph_dataset.get_node_feature(nodes, [1])
     """
 
@@ -114,7 +114,7 @@ class GraphData:
             numpy.ndarray, array of nodes.
 
         Examples:
-            >>> nodes = graph_dataset.get_all_nodes(0)
+            >>> nodes = graph_dataset.get_all_nodes(1)
 
         Raises:
             TypeError: If `node_type` is not integer.
@@ -175,8 +175,8 @@ class GraphData:
             numpy.ndarray, array of neighbors.
 
         Examples:
-            >>> nodes = graph_dataset.get_all_nodes(0)
+            >>> nodes = graph_dataset.get_all_nodes(1)
-            >>> neighbors = graph_dataset.get_all_neighbors(nodes, 0)
+            >>> neighbors = graph_dataset.get_all_neighbors(nodes, 2)
 
         Raises:
             TypeError: If `node_list` is not list or ndarray.
@@ -211,8 +211,8 @@ class GraphData:
             numpy.ndarray, array of neighbors.
 
         Examples:
-            >>> nodes = graph_dataset.get_all_nodes(0)
+            >>> nodes = graph_dataset.get_all_nodes(1)
-            >>> neighbors = graph_dataset.get_sampled_neighbors(nodes, [2, 2], [0, 0])
+            >>> neighbors = graph_dataset.get_sampled_neighbors(nodes, [2, 2], [2, 1])
 
         Raises:
             TypeError: If `node_list` is not list or ndarray.
@@ -240,8 +240,8 @@ class GraphData:
             numpy.ndarray, array of neighbors.
 
         Examples:
-            >>> nodes = graph_dataset.get_all_nodes(0)
+            >>> nodes = graph_dataset.get_all_nodes(1)
-            >>> neg_neighbors = graph_dataset.get_neg_sampled_neighbors(nodes, 5, 0)
+            >>> neg_neighbors = graph_dataset.get_neg_sampled_neighbors(nodes, 5, 2)
 
         Raises:
             TypeError: If `node_list` is not list or ndarray.
@@ -266,8 +266,8 @@ class GraphData:
             numpy.ndarray, array of features.
 
         Examples:
-            >>> nodes = graph_dataset.get_all_nodes(0)
+            >>> nodes = graph_dataset.get_all_nodes(1)
-            >>> features = graph_dataset.get_node_feature(nodes, [1])
+            >>> features = graph_dataset.get_node_feature(nodes, [2, 3])
 
         Raises:
             TypeError: If `node_list` is not list or ndarray.
@@ -347,7 +347,7 @@ class GraphData:
             numpy.ndarray, array of nodes.
 
         Examples:
-            >>> nodes = graph_dataset.random_walk([1,2], [1,2,1,2,1])
+            >>> nodes = graph_dataset.random_walk([1, 2], [1, 2, 1, 2, 1])
 
         Raises:
             TypeError: If `target_nodes` is not list or ndarray.

mindspore/dataset/engine/samplers.py

@@ -203,14 +203,12 @@ class Sampler(BuiltinSampler):
     dataset_size and num_samples will be set by dataset once a dataset iterator is created.
 
     Examples:
-        >>> import mindspore.dataset as ds
+        >>> class ReverseSampler(ds.Sampler):
+        ...     def __iter__(self):
+        ...         for i in range(self.dataset_size - 1, -1, -1):
+        ...             yield i
         >>>
-        >>> class ReverseSampler(ds,Sampler):
+        >>> ds = ds.ImageFolderDataset(image_folder_dataset_dir, sampler=ReverseSampler())
-        >>>     def __iter__(self):
-        >>>         for i in range(self.dataset_size - 1, -1, -1):
-        >>>             yield i
-        >>>
-        >>> ds = ds.ImageFolderDataset(path, sampler=ReverseSampler())
     """
 
     def __init__(self, num_samples=None):
@@ -698,15 +696,11 @@ class SubsetRandomSampler(SubsetSampler):
         num_samples (int, optional): Number of elements to sample (default=None, all elements).
 
     Examples:
-        >>> import mindspore.dataset as ds
-        >>>
-        >>> dataset_dir = "path/to/imagefolder_directory"
-        >>>
         >>> indices = [0, 1, 2, 3, 7, 88, 119]
         >>>
-        >>> # creates a SubsetRandomSampler, will sample from the provided indices
+        >>> # create a SubsetRandomSampler, will sample from the provided indices
         >>> sampler = ds.SubsetRandomSampler(indices)
-        >>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler)
+        >>> data = ds.ImageFolderDataset(image_folder_dataset_dir, num_parallel_workers=8, sampler=sampler)
 
     Raises:
         TypeError: If type of indices element is not a number.

mindspore/dataset/transforms/c_transforms.py

@@ -106,14 +106,15 @@ class TypeCast(cde.TypeCastOp):
     Examples:
         >>> import numpy as np
         >>> import mindspore.common.dtype as mstype
-        >>> from mindspore.dataset import GeneratorDataset
+        >>>
         >>> # Generate 1d int numpy array from 0 - 63
         >>> def generator_1d():
-        >>>     for i in range(64):
+        ...     for i in range(64):
         ...         yield (np.array([i]),)
-        >>> generator_dataset = GeneratorDataset(generator_1d,column_names='col')
+        >>>
+        >>> dataset = ds.GeneratorDataset(generator_1d, column_names='col')
         >>> type_cast_op = c_transforms.TypeCast(mstype.int32)
-        >>> generator_dataset = generator_dataset.map(operations=type_cast_op)
+        >>> dataset = dataset.map(operations=type_cast_op)
     """
 
     @check_de_type

mindspore/dataset/transforms/py_transforms.py

@@ -40,7 +40,7 @@ class OneHotOp:
         >>> # Assume that dataset has 10 classes, thus the label ranges from 0 to 9
         >>> transforms_list = [py_transforms.OneHotOp(num_classes=10, smoothing_rate=0.1)]
         >>> transform = py_transforms.Compose(transforms_list)
-        >>> mnist_dataset = mnist_dataset(input_columns=["label"], operations=transform)
+        >>> mnist_dataset = mnist_dataset.map(input_columns=["label"], operations=transform)
     """
 
     @check_one_hot_op