# tf.random.stateless_parameterized_truncated_normal

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/stateless_random_ops.py#L850-L921) |

Outputs random values from a truncated normal distribution.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.random.stateless_parameterized_truncated_normal`](https://www.tensorflow.org/api_docs/python/tf/random/stateless_parameterized_truncated_normal)

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    tf.random.stateless_parameterized_truncated_normal(
        shape, seed, means=0.0, stddevs=1.0, minvals=-2.0, maxvals=2.0, name=None
    )

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])

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| ## Args ||
|---|---|
| `shape` | A 1-D integer `Tensor` or Python array. The shape of the output tensor. |
| `seed` | A shape \[2\] 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). |

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| ## Returns ||
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| A tensor of the specified shape filled with random truncated normal values. ||

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