# tensorplay.functional.randn_like

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

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

Returns a tensor with the same size as input that is filled with
random numbers from a normal distribution with mean 0 and variance 1. Please refer to [tensorplay.randn()](/docs/generated/tensorplay.randn.html#tensorplay.randn) for the
sampling process of complex dtypes. tensorplay.randn_like(input) is equivalent to
tensorplay.randn(input.size(), 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.

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.