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@ -1105,9 +1105,11 @@ class ArgMaxWithValue(PrimitiveWithInfer):
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:math:`(x_1, x_2, ..., x_N)`.
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:math:`(x_1, x_2, ..., x_N)`.
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Outputs:
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Outputs:
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Tensor, corresponding index and maximum value of input tensor. If `keep_dims` is true, the output tensors shape
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tuple(Tensor), tuple of 2 tensors, corresponding index and maximum value of input tensor.
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- index (Tensor) - The index for maximum value of input tensor. If `keep_dims` is true, the output tensors shape
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is :math:`(x_1, x_2, ..., x_{axis-1}, 1, x_{axis+1}, ..., x_N)`. Else, the shape is
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is :math:`(x_1, x_2, ..., x_{axis-1}, 1, x_{axis+1}, ..., x_N)`. Else, the shape is
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:math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
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:math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
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- output_x (Tensor) - The maximum value of input tensor, the shape same as index.
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Examples:
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Examples:
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>>> input_x = Tensor(np.random.rand(5))
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>>> input_x = Tensor(np.random.rand(5))
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@ -2161,7 +2163,7 @@ class ScatterMax(PrimitiveWithInfer):
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Tensor, has the same shape and data type as `input_x`.
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Tensor, has the same shape and data type as `input_x`.
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Examples:
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Examples:
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>>> input_x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
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>>> input_x = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32), name="input_x")
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>>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
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>>> indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
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>>> update = Tensor(np.ones([2, 2, 3]) * 88, mindspore.float32)
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>>> update = Tensor(np.ones([2, 2, 3]) * 88, mindspore.float32)
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>>> scatter_max = P.ScatterMax()
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>>> scatter_max = P.ScatterMax()
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