/TensorFlow 2.4

# tf.random.stateless_parameterized_truncated_normal

Outputs random values from a truncated normal distribution.

The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

#### Examples:

Sample from a Truncated normal, with deferring shape parameters that broadcast.

```means = 0.
stddevs = tf.math.exp(tf.random.uniform(shape=[2, 3]))
minvals = [-1., -2., -1000.]
maxvals = [[10000.], [1.]]
y = tf.random.stateless_parameterized_truncated_normal(
shape=[10, 2, 3], seed=[7, 17],
means=means, stddevs=stddevs, minvals=minvals, maxvals=maxvals)
y.shape
TensorShape([10, 2, 3])
```
Args
`shape` A 1-D integer `Tensor` or Python array. The shape of the output tensor.
`seed` A shape  Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
`means` A `Tensor` or Python value of type `dtype`. The mean of the truncated normal distribution. This must broadcast with `stddevs`, `minvals` and `maxvals`, and the broadcasted shape must be dominated by `shape`.
`stddevs` A `Tensor` or Python value of type `dtype`. The standard deviation of the truncated normal distribution. This must broadcast with `means`, `minvals` and `maxvals`, and the broadcasted shape must be dominated by `shape`.
`minvals` A `Tensor` or Python value of type `dtype`. The minimum value of the truncated normal distribution. This must broadcast with `means`, `stddevs` and `maxvals`, and the broadcasted shape must be dominated by `shape`.
`maxvals` A `Tensor` or Python value of type `dtype`. The maximum value of the truncated normal distribution. This must broadcast with `means`, `stddevs` and `minvals`, and the broadcasted shape must be dominated by `shape`.
`name` A name for the operation (optional).
Returns
A tensor of the specified shape filled with random truncated normal values.