code: def trunc_normal_(
      tensor: Tensor,
      mean: float = 0.0,
      std: float = 1.0,
      a: float = -2.0,
      b: float = 2.0,
      generator: _Optional[torch.Generator] = None,
  ) -> Tensor:
      r"""Fill 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`.
  
      Args:
          tensor: an n-dimensional `torch.Tensor`
          mean: the mean of the normal distribution
          std: the standard deviation of the normal distribution
          a: the minimum cutoff value
          b: the maximum cutoff value
          generator: the torch Generator to sample from (default: None)
  
      Examples:
          >>> w = torch.empty(3, 5)
          >>> nn.init.trunc_normal_(w)
      """
      return _no_grad_trunc_normal_(
          tensor, mean, std, a, b, generator=generator
      )
  
imports: import math
  import warnings
  from torch import Tensor
  import torch
  from typing import Optional
label_desc: Use image node_id 9 for calling a global function with example usage: trunc_normal_(tensor, mean, std, a, b, generator) and returns: _no_grad_trunc_normal_