Fix bug about API of ops.

mindspore/ops/operations/array_ops.py

@@ -435,7 +435,7 @@ class Squeeze(PrimitiveWithInfer):
         ValueError: If the corresponding dimension of the specified axis does not equal to 1.
 
     Args:
-        axis (int): Specifies the dimension indexes of shape to be removed, which will remove
+        axis (Union[int, tuple(int)]): Specifies the dimension indexes of shape to be removed, which will remove
             all the dimensions that are equal to 1. If specified, it must be int32 or int64.
             Default: (), an empty tuple.
 
@@ -1427,7 +1427,8 @@ class UnsortedSegmentProd(PrimitiveWithInfer):
     Inputs:
         - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`.
           With float16, float32 or int32 data type.
-        - **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`. Data type must be int32.
+        - **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`, the value should be >= 0.
+          Data type must be int32.
         - **num_segments** (int) - The value spcifies the number of distinct `segment_ids`,
           should be greater than 0.
 

mindspore/ops/operations/nn_ops.py

@@ -3760,12 +3760,12 @@ class ApplyAdagradV2(PrimitiveWithInfer):
         update_slots (bool): If `True`, `accum` will be updated. Default: True.
 
     Inputs:
-        - **var** (Parameter) - Variable to be updated. With float32 or float16 data type.
+        - **var** (Parameter) - Variable to be updated. With float32 data type.
         - **accum** (Parameter) - Accum to be updated. The shape and dtype should be the same as `var`.
-          With float32 or float16 data type.
-        - **lr** (Union[Number, Tensor]) - The learning rate value, should be scalar. With float32 or float16 data type.
+          With float32 data type.
+        - **lr** (Union[Number, Tensor]) - The learning rate value, should be scalar. With float32 data type.
         - **grad** (Tensor) - A tensor for gradient. The shape and dtype should be the same as `var`.
-          With float32 or float16 data type.
+          With float32 data type.
 
     Outputs:
         Tuple of 2 Tensor, the updated parameters.
@@ -3817,9 +3817,8 @@ class ApplyAdagradV2(PrimitiveWithInfer):
 
     def infer_dtype(self, var_dtype, accum_dtype, lr_dtype, grad_dtype):
         args = {'var': var_dtype, 'accum': accum_dtype, 'grad': grad_dtype}
-        valid_types = [mstype.float16, mstype.float32]
-        validator.check_tensor_type_same(args, valid_types, self.name)
-        validator.check_scalar_or_tensor_type_same({'lr': lr_dtype}, valid_types, self.name)
+        validator.check_tensor_type_same(args, [mstype.float32], self.name)
+        validator.check_scalar_or_tensor_type_same({'lr': lr_dtype}, [mstype.float32], self.name)
         return var_dtype, accum_dtype