fix examples issues 3

mindspore/python/mindspore/ops/composite/math_ops.py

@@ -364,6 +364,7 @@ def dot(x1, x2):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import numpy as np
         >>> import mindspore
         >>> from mindspore import Tensor, ops
         >>> input_x1 = Tensor(np.ones(shape=[2, 3]), mindspore.float32)

mindspore/python/mindspore/ops/function/array_func.py

@@ -3800,8 +3800,6 @@ def broadcast_to(x, shape):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        >>> import mindspore.ops as ops
-        >>> from mindspore import Tensor
         >>> shape = (2, 3)
         >>> x = Tensor(np.array([1, 2, 3]).astype(np.float32))
         >>> output = ops.broadcast_to(x, shape)

mindspore/python/mindspore/ops/function/math_func.py

@@ -7057,10 +7057,11 @@ def cumprod(input, dim, dtype=None):
     Supported Platforms:
         ``Ascend`` ``CPU`` ``GPU``
 
-    >>> x = Tensor(np.array([1, 2, 3], np.float32))
-    >>> output = ops.cumprod(x, 0)
-    >>> print(output)
-    [1. 2. 6.]
+    Examples:
+        >>> x = Tensor(np.array([1, 2, 3], np.float32))
+        >>> output = ops.cumprod(x, 0)
+        >>> print(output)
+        [1. 2. 6.]
     """
     cumprod_op = _get_cache_prim(P.CumProd)()
     output = cumprod_op(input, dim)

mindspore/python/mindspore/ops/operations/comm_ops.py

@@ -62,6 +62,8 @@ class ReduceOp:
 
             This example should be run with multiple devices.
 
+        >>> import numpy as np
+        >>> import mindspore
         >>> from mindspore.communication import init
         >>> from mindspore import Tensor, ops, nn
         >>> from mindspore.ops import ReduceOp

mindspore/python/mindspore/ops/operations/custom_ops.py

@@ -315,8 +315,8 @@ class Custom(ops.PrimitiveWithInfer):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        >>> import mindspore.ops as ops
         >>> import numpy as np
+        >>> from mindspore import Tensor, ops
         >>> from mindspore.ops import CustomRegOp, custom_info_register, DataType, kernel
         >>> from mindspore import dtype as mstype
         >>> from mindspore.nn import Cell

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -1153,7 +1153,7 @@ class Bucketize(Primitive):
         >>> class Bucketize(nn.Cell):
         ...     def __init__(self, boundaries):
         ...         super().__init__()
-        ...         self.bucketize = op.Bucketize(boundaries=boundaries)
+        ...         self.bucketize = ops.Bucketize(boundaries=boundaries)
         ...     def construct(self, input):
         ...         return self.bucketize(input)
         >>> input = Tensor(np.array([[3, 6, 9], [3, 6, 9]]).astype(np.int32))

mindspore/python/mindspore/ops/operations/nn_ops.py

@@ -8000,7 +8000,7 @@ class CTCLossV2(Primitive):
         >>> targets = Tensor(np.array([[0, 1]]), mstype.int32)
         >>> input_lengths = Tensor(np.array([2]), mstype.int32)
         >>> target_lengths = Tensor(np.array([1]), mstype.int32)
-        >>> CTCLossV2 = op.CTCLossV2(blank=0, reduction='none', zero_infinity=False)
+        >>> CTCLossV2 = ops.CTCLossV2(blank=0, reduction='none', zero_infinity=False)
         >>> neg_log_hood, log_alpha = CTCLossV2(
         ...     log_probs, targets, input_lengths, target_lengths)
         >>> print(neg_log_hood)