# tensorplay.functional.full_like

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.full_like.html

```python
tensorplay.functional.full_like(input, fill_value, \*, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False, memory_format=tensorplay.preserve_format) → Tensor
```

Returns a tensor with the same size as input filled with fill_value.
tensorplay.full_like(input, fill_value) is equivalent to
tensorplay.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the size of input will determine size of the output tensor.

- fill_value – the number to fill the output tensor with.

Keyword Arguments:

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType), optional) – the desired data type of returned Tensor. Default: if None, defaults to the dtype of input.

- layout (tensorplay.layout, optional) – the desired layout of returned tensor. Default: if None, defaults to the layout of input.

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device), optional) – the desired device of returned tensor. Default: if None, defaults to the device of input.

- requires_grad ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – If autograd should record operations on the returned tensor. Default: False.

- memory_format ([tensorplay.memory_format](/docs/generated/tensorplay.MemoryFormat.html#tensorplay.MemoryFormat), optional) – the desired memory format of returned Tensor. Default: tensorplay.preserve_format.

Example:

```
>>> x = tensorplay.ones(2, 3)
>>> tensorplay.full_like(x, 3.141592)
tensor([[ 3.1416,  3.1416,  3.1416],
        [ 3.1416,  3.1416,  3.1416]])
>>> tensorplay.full_like(x, 7)
tensor([[7., 7., 7.],
        [7., 7., 7.]])
>>> tensorplay.full_like(x, 0.5, dtype=tensorplay.int32)
tensor([[0, 0, 0],
        [0, 0, 0]], dtype=tensorplay.int32)
>>> y = tensorplay.randn(3, 4, dtype=tensorplay.float64)
>>> tensorplay.full_like(y, -1.0)
tensor([[-1., -1., -1., -1.],
        [-1., -1., -1., -1.],
        [-1., -1., -1., -1.]], dtype=tensorplay.float64)
```