code: def _modified_bessel_1(x: torch.Tensor) -> torch.Tensor:
      x = torch.as_tensor(
          x,
          dtype=torch.float,
          device=x.device if isinstance(x, torch.Tensor) else None,
      )
      if torch.abs(x) < 3.75:
          y = x * x / 14.0625
          _coef = [
              0.32411e-3,
              0.301532e-2,
              0.2658733e-1,
              0.15084934,
              0.51498869,
              0.87890594,
              0.5,
          ]
          return torch.abs(x) * polyval(_coef, y)
      ax = torch.abs(x)
      y = 3.75 / ax
      _coef = [
          -0.420059e-2,
          0.1787654e-1,
          -0.2895312e-1,
          0.2282967e-1,
          -0.1031555e-1,
          0.163801e-2,
          -0.362018e-2,
          -0.3988024e-1,
          0.39894228,
      ]
      ans = polyval(_coef, y) * torch.exp(ax) / torch.sqrt(ax)
      return -ans if x < 0.0 else ans
  
file_path: MONAI/monai/networks/layers/convutils.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import numpy
  import torch
label_desc: Use image node_id 7 for calling a global function with example usage: _modified_bessel_1(x) and returns: unknown, unknown