def trunc_normal_(tensor: Tensor, mean: float=0.0, std: float=1.0, a: float=(- 2.0), b: float=2.0) -> Tensor:
    'Fills the input Tensor with values drawn from a truncated\n    normal distribution. The values are effectively drawn from the\n    normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n    with values outside :math:`[a, b]` redrawn until they are within\n    the bounds. The method used for generating the random values works\n    best when :math:`a \\leq \\text{mean} \\leq b`.\n\n    Modified from\n    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py\n\n    Args:\n        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.\n        mean (float): the mean of the normal distribution.\n        std (float): the standard deviation of the normal distribution.\n        a (float): the minimum cutoff value.\n        b (float): the maximum cutoff value.\n    '
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.

Modified from
https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py

Args:
    tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
    mean (float): the mean of the normal distribution.
    std (float): the standard deviation of the normal distribution.
    a (float): the minimum cutoff value.
    b (float): the maximum cutoff value.
def trunc_normal_(tensor: Tensor, mean: float=0.0, std: float=1.0, a: float=(- 2.0), b: float=2.0) -> Tensor:
    'Fills the input Tensor with values drawn from a truncated\n    normal distribution. The values are effectively drawn from the\n    normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n    with values outside :math:`[a, b]` redrawn until they are within\n    the bounds. The method used for generating the random values works\n    best when :math:`a \\leq \\text{mean} \\leq b`.\n\n    Modified from\n    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py\n\n    Args:\n        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.\n        mean (float): the mean of the normal distribution.\n        std (float): the standard deviation of the normal distribution.\n        a (float): the minimum cutoff value.\n        b (float): the maximum cutoff value.\n    '
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)