code: def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
      """Tensor initialization with truncated normal distribution.
      Based on:
      https://github.com/rwightman/pytorch-image-models
  
      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
      """
  
      if std <= 0:
          raise ValueError(
              "the standard deviation should be greater than zero."
          )
  
      if a >= b:
          raise ValueError(
              "minimum cutoff value (a) should be smaller than maximum cutoff value (b)."
          )
  
      return _no_grad_trunc_normal_(tensor, mean, std, a, b)
  
imports: from __future__ import annotations
  import math
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: trunc_normal_(tensor, mean, std, a, b) and returns: _no_grad_trunc_normal_