# tensorplay.randn

Source: https://www.tensorplay.cn/docs/generated/tensorplay.randn.html

```python
tensorplay.randn()
```

randn(*size, *, generator=None, out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor

Returns a tensor filled with random numbers from a normal distribution
with mean 0 and variance 1 (also called the standard normal
distribution).

$$\text{out}_{i} \sim \mathcal{N}(0, 1)$$

For complex dtypes, the tensor is i.i.d. sampled from a [complex normal distribution](https://en.wikipedia.org/wiki/Complex_normal_distribution) with zero mean and
unit variance as

$$\text{out}_{i} \sim \mathcal{CN}(0, 1)$$

This is equivalent to separately sampling the real $(\operatorname{Re})$ and imaginary
$(\operatorname{Im})$ part of $\text{out}_i$ as

$$\operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad
\operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2})$$

The shape of the tensor is defined by the variable argument [size](/docs/generated/tensorplay.size.html#tensorplay.size).

Parameters:

size ([int](https://docs.python.org/3/builtins/functions.html#int)...) – a sequence of integers defining the shape of the output tensor.
Can be a variable number of arguments or a collection like a list or tuple.

Keyword Arguments:

- generator ([tensorplay.Generator](/docs/generated/tensorplay.Generator.html#tensorplay.Generator), optional) – a pseudorandom number generator for sampling

- out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType), optional) – the desired data type of returned tensor. Default: if None, uses a global default (see [tensorplay.set_default_dtype()](/docs/generated/tensorplay.set_default_dtype.html#tensorplay.set_default_dtype)).

- layout (tensorplay.layout, optional) – the desired layout of returned Tensor. Default: tensorplay.strided.

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device), optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see [tensorplay.set_default_device()](/docs/generated/tensorplay.set_default_device.html#tensorplay.set_default_device)). [device](/docs/generated/tensorplay.Device.html#tensorplay.Device) will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

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

- pin_memory ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

```
>>> tensorplay.randn(4)
tensor([-2.1436,  0.9966,  2.3426, -0.6366])
>>> tensorplay.randn(2, 3)
tensor([[ 1.5954,  2.8929, -1.0923],
        [ 1.1719, -0.4709, -0.1996]])
```