Computes the remainder of dividing the first input tensor by the second input tensor element-wise.
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, both dtypes cannot be 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]) - The first input is a number or a tensor whose data type is number.
input_y (Union[Tensor, Number]) - When the first input is a tensor, The second input could be a number or a tensor whose data type is number. When the first input is a number, the second input must be a tensor whose data type is number.
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.
ValueError – When input_x and input_y are not the same dtype.
- Supported Platforms:
>>> input_x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) >>> input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) >>> mod = ops.Mod() >>> output = mod(input_x, input_y) >>> print(output) [-1. 1. 0.]