Computes a safe divide and returns 0 if the y is zero.
Inputs of input_x and input_y comply with the implicit type conversion rules to make the data types consistent. The inputs must be two tensors or one tensor and one scalar. When the inputs are two tensors, dtypes of them cannot be both bool, and the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.
input_x (Union[Tensor, Number, bool]) - The first input is a number or a bool or a tensor whose data type is number or bool.
input_y (Union[Tensor, Number, bool]) - The second input is a number or a bool when the first input is a tensor or a tensor whose data type is number or bool.
Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.
TypeError – If neither input_x nor input_y is a Tensor.
- Supported Platforms:
>>> input_x = Tensor(np.array([-1.0, 0., 1.0, 5.0, 6.0]), mindspore.float32) >>> input_y = Tensor(np.array([0., 0., 0., 2.0, 3.0]), mindspore.float32) >>> div_no_nan = ops.DivNoNan() >>> output = div_no_nan(input_x, input_y) >>> print(output) [0. 0. 0. 2.5 2. ]