# repeat

`Graph.repeat(input, repeats, axis=None, *, name=None)`

Repeat elements of a tensor.

### Parameters

**input**(*np.ndarray**or**Tensor*) – The tensor whose elements you want to repeat.**repeats**(*int**or**list**[**int**]*) – The number of times to repeat each element. If you pass a single int or singleton list, that number of repetitions is applied to each element. Otherwise, you must pass a list with the same length as input along the specified axis (or the same total length as input if you omit axis).**axis**(*int**or**None**,**optional*) – The axis along which you want to repeat elements. If you omit this value then the input is first flattened, and the repetitions applied to the flattened tensor.**name**(*str**or**None**,**optional*) – The name of the node.

### Returns

The repeated tensor. The result has the same shape as input except along axis, where its size is either the sum of repeats (if repeats is a list with at least two elements) or the product of the original size along axis with repeats (if repeats is a single int or singleton list). If axis is None then the output is 1D, with the sizes as described above applied to the flattened input tensor.

### Return type

## Examples

```
>>> x = np.array([1, 2, 3])
>>> y = np.array([[1, 2, 3], [4, 5, 6]])
```

Duplicate each entry in an array once.

```
>>> graph.repeat(x, 2, axis=0, name="a")
<Tensor: name="a", operation_name="repeat", shape=(6,)>
>>> result = bo.execute_graph(graph=graph, output_node_names="a")
>>> result["output"]["a"]["value"]
array([1, 1, 2, 2, 3, 3])
```

Create a new array with different repetitions for each element in the original array along its second dimension.

```
>>> graph.repeat(x, [2, 3, 4], axis=0, name="b")
<Tensor: name="b", operation_name="repeat", shape=(9,)>
>>> result = bo.execute_graph(graph=graph, output_node_names="b")
>>> result["output"]["b"]["value"]
array([1, 1, 2, 2, 2, 3, 3, 3, 3])
```

Duplicate each entry in an array along its second dimension.

```
>>> graph.repeat(y, 2, axis=1, name="c")
<Tensor: name="c", operation_name="repeat", shape=(2, 6)>
>>> result = bo.execute_graph(graph=graph, output_node_names="c")
>>> result["output"]["c"]["value"]
array([[1, 1, 2, 2, 3, 3],
[4, 4, 5, 5, 6, 6]])
```

Create a new array with different repetitions for each element in the original array along its first dimension.

```
>>> graph.repeat(y, [2, 3], axis=0, name="d")
<Tensor: name="d", operation_name="repeat", shape=(5, 3)>
>>> result = bo.execute_graph(graph=graph, output_node_names="d")
>>> result["output"]["d"]["value"]
array([[1, 2, 3],
[1, 2, 3],
[4, 5, 6],
[4, 5, 6],
[4, 5, 6]])
```