code: def _no_grad_trunc_normal(
      tensor: torch.Tensor,
      mean: float,
      std: float,
      a: float,
      b: float,
  ) -> torch.Tensor:
      """Initializes the input tensor with a truncated normal distribution.
  
      This method is based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
  
      Args:
          tensor:
              The tensor to initialize.
          mean:
              Mean of the distribution.
          std:
              Standard deviation of the distribution.
          a:
              Minimum value of the distribution, values below will be clamped.
          b:
              Maximum value of the distribution, values above will be clamped.
  
      """
  
      def norm_cdf(x):
          # Computes standard normal cumulative distribution function
          return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
  
      if (mean < a - 2 * std) or (mean > b + 2 * std):
          warnings.warn(
              "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
              "The distribution of values may be incorrect.",
              stacklevel=2,
          )
  
      with torch.no_grad():
          # Values are generated by using a truncated uniform distribution and
          # then using the inverse CDF for the normal distribution.
          # Get upper and lower cdf values
          l = norm_cdf((a - mean) / std)
          u = norm_cdf((b - mean) / std)
  
          # Uniformly fill tensor with values from [l, u], then translate to
          # [2l-1, 2u-1].
          tensor.uniform_(2 * l - 1, 2 * u - 1)
  
          # Use inverse cdf transform for normal distribution to get truncated
          # standard normal
          tensor.erfinv_()
  
          # Transform to proper mean, std
          tensor.mul_(std * math.sqrt(2.0))
          tensor.add_(mean)
  
          # Clamp to ensure it's in the proper range
          tensor.clamp_(min=a, max=b)
          return tensor
  
imports: import math
  import warnings
  from typing import Iterable, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from numpy.typing import NDArray
  from torch.nn import Module
  from torch.nn.modules.batchnorm import _BatchNorm
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 10 for calling a global function with example usage: _no_grad_trunc_normal(tensor, mean, std, a, b) and returns: unknown, tensor