def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
    """Draw samples from a truncated normal distribution.

    The values are drawn from a normal distribution with specified mean and
    standard deviation, discarding and re-drawing any samples that are more
    than two standard deviations from the mean.

    Args:
        shape: The shape of the random values to generate.
        mean: Float, defaults to 0. Mean of the random values to generate.
        stddev: Float, defaults to 1. Standard deviation of the random values
            to generate.
        dtype: Optional dtype of the tensor. Only floating point types are
            supported. If not specified, `keras.config.floatx()` is used,
            which defaults to `float32` unless you configured it otherwise (via
            `keras.config.set_floatx(float_dtype)`)
        seed: Optional Python integer or instance of
           `keras.random.SeedGenerator`.
            By default, the `seed` argument is `None`, and an internal global
            `keras.random.SeedGenerator` is used. The `seed` argument can be
            used to ensure deterministic (repeatable) random number generation.
            Note that passing an integer as the `seed` value will produce the
            same random values for each call. To generate different random
            values for repeated calls, an instance of
            `keras.random.SeedGenerator` must be provided as the `seed` value.
            Remark concerning the JAX backend: When tracing functions with the
            JAX backend the global `keras.random.SeedGenerator` is not
            supported. Therefore, during tracing the default value seed=None
            will produce an error, and a `seed` argument must be provided.
    """
    return backend.random.truncated_normal(
        shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed
    )
Draw samples from a truncated normal distribution.

    The values are drawn from a normal distribution with specified mean and
    standard deviation, discarding and re-drawing any samples that are more
    than two standard deviations from the mean.

    Args:
        shape: The shape of the random values to generate.
        mean: Float, defaults to 0. Mean of the random values to generate.
        stddev: Float, defaults to 1. Standard deviation of the random values
            to generate.
        dtype: Optional dtype of the tensor. Only floating point types are
            supported. If not specified, `keras.config.floatx()` is used,
            which defaults to `float32` unless you configured it otherwise (via
            `keras.config.set_floatx(float_dtype)`)
        seed: Optional Python integer or instance of
           `keras.random.SeedGenerator`.
            By default, the `seed` argument is `None`, and an internal global
            `keras.random.SeedGenerator` is used. The `seed` argument can be
            used to ensure deterministic (repeatable) random number generation.
            Note that passing an integer as the `seed` value will produce the
            same random values for each call. To generate different random
            values for repeated calls, an instance of
            `keras.random.SeedGenerator` must be provided as the `seed` value.
            Remark concerning the JAX backend: When tracing functions with the
            JAX backend the global `keras.random.SeedGenerator` is not
            supported. Therefore, during tracing the default value seed=None
            will produce an error, and a `seed` argument must be provided.
    
keras/src/random/random.py