# mindspore.ops.Select

class mindspore.ops.Select[source]

Returns the selected elements, either from input $$x$$ or input $$y$$, depending on the condition.

Given a tensor as input, this operation inserts a dimension of 1 at the dimension, it was invalid when both $$x$$ and $$y$$ are none. Keep in mind that the shape of the output tensor can vary depending on how many true values are in the input. Indexes are output in row-first order.

The conditional tensor acts as an optional compensation (mask), which determines whether the corresponding element / row in the output must be selected from $$x$$ (if true) or $$y$$ (if false) based on the value of each element.

It can be defined as:

$\begin{split}out_i = \begin{cases} x_i, & \text{if } condition_i \\ y_i, & \text{otherwise} \end{cases}\end{split}$

If condition is a vector, then $$x$$ and $$y$$ are higher-dimensional matrices, then it chooses to copy that row (external dimensions) from $$x$$ and $$y$$. If condition has the same shape as $$x$$ and $$y$$, you can choose to copy these elements from $$x$$ and $$y$$.

Inputs:
• input_cond (Tensor[bool]) - The shape is $$(x_1, x_2, ..., x_N, ..., x_R)$$. The condition tensor, decides which element is chosen.

• input_x (Tensor) - The shape is $$(x_1, x_2, ..., x_N, ..., x_R)$$. The first input tensor.

• input_y (Tensor) - The shape is $$(x_1, x_2, ..., x_N, ..., x_R)$$. The second input tensor.

Outputs:

Tensor, has the same shape as input_x. The shape is $$(x_1, x_2, ..., x_N, ..., x_R)$$.

Raises
• TypeError – If input_x or input_y is not a Tensor.

• ValueError – If shape of input_x is not equal to shape of input_y or shape of input_cond.

Supported Platforms:

Ascend GPU CPU

Examples

>>> select = ops.Select()
>>> input_cond = Tensor([True, False])
>>> input_x = Tensor([2,3], mindspore.float32)
>>> input_y = Tensor([1,2], mindspore.float32)
>>> output = select(input_cond, input_x, input_y)
>>> print(output)
[2. 2.]