code: def _no_grad_trunc_normal_(tensor, mean, std, a, b):
      """Tensor initialization with truncated normal distribution.
      Based on:
      https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
      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.
      """
  
      def norm_cdf(x):
          return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
  
      with torch.no_grad():
          l = norm_cdf((a - mean) / std)
          u = norm_cdf((b - mean) / std)
          tensor.uniform_(2 * l - 1, 2 * u - 1)
          tensor.erfinv_()
          tensor.mul_(std * math.sqrt(2.0))
          tensor.add_(mean)
          tensor.clamp_(min=a, max=b)
          return tensor
  
imports: from __future__ import annotations
  import math
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: _no_grad_trunc_normal_(tensor, mean, std, a, b) and returns: unknown, tensor