code: def _modified_bessel_0(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
          return polyval(
              [
                  0.45813e-2,
                  0.360768e-1,
                  0.2659732,
                  1.2067492,
                  3.0899424,
                  3.5156229,
                  1.0,
              ],
              y,
          )
      ax = torch.abs(x)
      y = 3.75 / ax
      _coef = [
          0.392377e-2,
          -0.1647633e-1,
          0.2635537e-1,
          -0.2057706e-1,
          0.916281e-2,
          -0.157565e-2,
          0.225319e-2,
          0.1328592e-1,
          0.39894228,
      ]
      return polyval(_coef, y) * torch.exp(ax) / torch.sqrt(ax)
  
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 6 for calling a global function with example usage: _modified_bessel_0(x) and returns: unknown, polyval