mindspore.mint.greater_equal

mindspore.mint.greater_equal(input, other) → Tensor[source]

Computes the boolean value of \(input >= other\) element-wise.

\[\begin{split}out_{i} =\begin{cases} & \text{True, if } input_{i}>=other_{i} \\ & \text{False, if } input_{i}<other_{i} \end{cases}\end{split}\]

Note

Inputs of input and other 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 bool at the same time, and the shapes of them can be broadcast.
When the inputs are one tensor and one scalar, the scalar could only be a constant.
Broadcasting is supported.
If the input Tensor can be broadcast, the low dimension will be extended to the corresponding high dimension in another input by copying the value of the dimension.

Parameters

input (Union[Tensor, Number]) – The first input is a number or a tensor whose data type is number or bool_.
other (Union[Tensor, Number]) – Second input. When the first input is a Tensor, the second input should be a Number, or a Tensor of the number or bool_ data type. When the first input is a Scalar, the second input must be a Tensor of number or bool_ data type.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is bool.

Raises

TypeError – If neither input nor other is a Tensor.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.array([1, 2, 3]), mindspore.int32)
>>> other = Tensor(np.array([1, 1, 4]), mindspore.int32)
>>> output = mint.greater_equal(input, other)
>>> print(output)
[True True False]
>>> y = 2.1
>>> output = mint.greater_equal(input, other)
>>> print(output)
[False False True]