(l-onnx-doc-GreaterOrEqual)=

# GreaterOrEqual


(l-onnx-op-greaterorequal-16)=

## GreaterOrEqual - 16

### Version

- **name**: [GreaterOrEqual (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#GreaterOrEqual)
- **domain**: `main`
- **since_version**: `16`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 16**.

### Summary

Returns the tensor resulted from performing the `greater_equal` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).

This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 16]
>
GreaterOrEqual (A, B) => (C)
{
   O1 = Greater (A, B)
   O2 = Equal (A, B)
   C = Or (O1, O2)
}
```

### Inputs

- **A** (heterogeneous) - **T**:

  First input operand for the logical operator.
- **B** (heterogeneous) - **T**:

  Second input operand for the logical operator.

### Outputs

- **C** (heterogeneous) - **T1**:

  Result tensor.

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input types to all numeric tensors.
* **T1** in ( `tensor(bool)` ):

  Constrain output to boolean tensor.

### Examples

#### default

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "GreaterOrEqual",
    inputs=["x", "y"],
    outputs=["greater_equal"],
)

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal")

x = np.random.randn(3, 4, 5).astype(np.int8)
y = np.random.randn(3, 4, 5).astype(np.int8)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int8")

x = np.random.randn(3, 4, 5).astype(np.int16)
y = np.random.randn(3, 4, 5).astype(np.int16)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int16")

x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint8")

x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint16")

x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint32")

x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint64")
```

#### _greater_broadcast

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "GreaterOrEqual",
    inputs=["x", "y"],
    outputs=["greater_equal"],
)

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast")
```

```{toctree}
text_diff_GreaterOrEqual_12_16
```

(l-onnx-op-greaterorequal-12)=

## GreaterOrEqual - 12

### Version

- **name**: [GreaterOrEqual (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#GreaterOrEqual)
- **domain**: `main`
- **since_version**: `12`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

This version of the operator has been available
**since version 12**.

### Summary

Returns the tensor resulted from performing the `greater_equal` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).

This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 12]
>
GreaterOrEqual (A, B) => (C)
{
   O1 = Greater (A, B)
   O2 = Equal (A, B)
   C = Or (O1, O2)
}
```

### Inputs

- **A** (heterogeneous) - **T**:

  First input operand for the logical operator.
- **B** (heterogeneous) - **T**:

  Second input operand for the logical operator.

### Outputs

- **C** (heterogeneous) - **T1**:

  Result tensor.

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input types to all numeric tensors.
* **T1** in ( `tensor(bool)` ):

  Constrain output to boolean tensor.