usr mindspore. instead of mstype.

mindspore/common/api.py

@@ -230,8 +230,8 @@ def ms_function(fn=None, obj=None, input_signature=None):
         >>>     z = F.tensor_add(x, y)
         >>>     return z
         >>>
-        >>> @ms_function(input_signature=(MetaTensor(mstype.float32, (1, 1, 3, 3)),
-        >>>                               MetaTensor(mstype.float32, (1, 1, 3, 3))))
+        >>> @ms_function(input_signature=(MetaTensor(mindspore.float32, (1, 1, 3, 3)),
+        >>>                               MetaTensor(mindspore.float32, (1, 1, 3, 3))))
         >>> def tensor_add_with_sig(x, y):
         >>>     z = F.tensor_add(x, y)
         >>>     return z

mindspore/common/initializer.py

@@ -282,7 +282,7 @@ def initializer(init, shape=None, dtype=mstype.float32):
         Tensor, initialized tensor.
 
     Examples:
-        >>> tensor = initializer('ones', [1, 2, 3], mstype.float32)
+        >>> tensor = initializer('ones', [1, 2, 3], mindspore.float32)
     """
     if not isinstance(init, (Tensor, numbers.Number, str, Initializer)):
         raise TypeError('Unsupported init type.')

mindspore/dataset/engine/datasets.py

@@ -1814,7 +1814,7 @@ class TFRecordDataset(SourceDataset):
         >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files)
         >>> # 2) get all rows from dataset_files with user-defined schema:
         >>> schema = ds.Schema()
-        >>> schema.add_column('col_1d', de_type=mstype.int64, shape=[2])
+        >>> schema.add_column('col_1d', de_type=mindspore.int64, shape=[2])
         >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema=schema)
         >>> # 3) get all rows from dataset_files with schema file "./schema.json":
         >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema="./schema.json")
@@ -2325,7 +2325,7 @@ class Schema:
         >>> import mindspore.common.dtype as mstype
         >>> # create schema, specify column name, mindspore.dtype and shape of the column
         >>> schema = ds.Schema()
-        >>> schema.add_column('col1', de_type=mstype.int64, shape=[2])
+        >>> schema.add_column('col1', de_type=mindspore.int64, shape=[2])
     """
 
     def __init__(self, schema_file=None):

mindspore/ops/operations/array_ops.py

@@ -1535,7 +1535,8 @@ class StridedSlice(PrimitiveWithInfer):
             - Finally, the output is [3, 3, 3].
 
     Examples
-        >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]])
+        >>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]],
+        >>>                   [[5, 5, 5], [6, 6, 6]]], mindspore.float32)
         >>> slice = StridedSlice()
         >>> output = slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1))
         >>> output.shape()
@@ -2067,7 +2068,7 @@ class SpaceToBatch(PrimitiveWithInfer):
         >>> block_size = 2
         >>> paddings = [[0, 0], [0, 0]]
         >>> space_to_batch = P.SpaceToBatch(block_size, paddings)
-        >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mstype.float32)
+        >>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
         >>> space_to_batch(x)
         [[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]]
 
@@ -2135,7 +2136,7 @@ class BatchToSpace(PrimitiveWithInfer):
         >>> block_size = 2
         >>> crops = [[0, 0], [0, 0]]
         >>> op = P.BatchToSpace(block_size, crops)
-        >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mstype.float32)
+        >>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32)
         >>> output = op(x)
         [[[[1., 2.], [3., 4.]]]]
 

mindspore/ops/operations/math_ops.py

@@ -1996,8 +1996,8 @@ class Atan2(_MathBinaryOp):
         Tensor, the shape is same as the shape after broadcasting, and the data type is same as 'input_x'.
 
     Examples:
-         >>> input_x = Tensor(np.array([[0, 1]]), mstype.float32)
-         >>> input_y = Tensor(np.array([[1, 1]]), mstype.float32)
+         >>> input_x = Tensor(np.array([[0, 1]]), mindspore.float32)
+         >>> input_y = Tensor(np.array([[1, 1]]), mindspore.float32)
          >>> atan2 = P.Atan2()
          >>> atan2(input_x, input_y)
          [[0. 0.7853982]]

mindspore/train/model.py

@@ -528,7 +528,7 @@ class Model:
             Tensor, array(s) of predictions.
 
         Examples:
-            >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mstype.float32)
+            >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
             >>> model = Model(Net())
             >>> model.predict(input_data)
         """