code: def apply_safety_factor(
      dtype,
      *,
      backend: str,
      min_value=None,
      max_value=None,
      abs_smallest_val=None,
      small_abs_safety_factor=1.1,
      large_abs_safety_factor=1.1,
      safety_factor_scale="linear",
  ):
      """Apply safety factor scaling to numeric data type.
  
      Parameters
      ----------
      dtype
          the data type to apply safety factor scaling to.
      min_value
          the minimum value of the data type.
      max_value
          the maximum value of the data type.
      abs_smallest_val
          the absolute smallest representable value of the data type.
      large_abs_safety_factor
          the safety factor to apply to the maximum value.
      small_abs_safety_factor
          the safety factor to apply to the minimum value.
      safety_factor_scale
          the scale to apply the safety factor to, either 'linear' or 'log'.
  
      Returns
      -------
          A tuple of the minimum value, maximum value and absolute smallest representable
      """
      assert (
          small_abs_safety_factor >= 1
      ), "small_abs_safety_factor must be >= 1"
      assert (
          large_abs_safety_factor >= 1
      ), "large_value_safety_factor must be >= 1"
  
      if "float" in dtype or "complex" in dtype:
          kind_dtype = "float"
          if mod_backend[backend]:
              proc, input_queue, output_queue = mod_backend[backend]
              input_queue.put(
                  ("dtype_info_helper", backend, kind_dtype, dtype)
              )
              dtype_info = output_queue.get()
          else:
              dtype_info = general_helpers_dtype_info_helper(
                  backend=backend, kind_dtype=kind_dtype, dtype=dtype
              )
      elif "int" in dtype:
          kind_dtype = "int"
          if mod_backend[backend]:
              proc, input_queue, output_queue = mod_backend[backend]
              input_queue.put(
                  ("dtype_info_helper", backend, kind_dtype, dtype)
              )
              dtype_info = output_queue.get()
          else:
              dtype_info = general_helpers_dtype_info_helper(
                  backend=backend, kind_dtype=kind_dtype, dtype=dtype
              )
      else:
          raise TypeError(
              f"{dtype} is not a valid numeric data type only integers and floats"
          )
  
      if min_value is None:
          # 0th index is max, 1st is min, 2nd is smallest_normal
          min_value = dtype_info[1]
      if max_value is None:
          max_value = dtype_info[0]
  
      if safety_factor_scale == "linear":
          min_value = min_value / large_abs_safety_factor
          max_value = max_value / large_abs_safety_factor
          if kind_dtype == "float" and not abs_smallest_val:
              abs_smallest_val = dtype_info[2] * small_abs_safety_factor
      elif safety_factor_scale == "log":
          min_sign = math.copysign(1, min_value)
          min_value = (
              abs(min_value) ** (1 / large_abs_safety_factor) * min_sign
          )
          max_sign = math.copysign(1, max_value)
          max_value = (
              abs(max_value) ** (1 / large_abs_safety_factor) * max_sign
          )
          if kind_dtype == "float" and not abs_smallest_val:
              m, e = math.frexp(dtype_info[2])
              abs_smallest_val = m * (
                  2 ** (e / small_abs_safety_factor)
              )
      else:
          raise ValueError(
              f"{safety_factor_scale} is not a valid safety factor scale."
              " use 'log' or 'linear'."
          )
      if kind_dtype == "int":
          return int(min_value), int(max_value), None
      return min_value, max_value, abs_smallest_val
  
imports: from hypothesis import assume, strategies
  from typing import Tuple
  from functools import lru_cache
  import math
  import numpy
  import ivy
  from .None import array_helpers, number_helpers, dtype_helpers
  from ..pipeline_helper import WithBackendContext
  from ivy.functional.ivy.layers import _deconv_length
  from ..globals import mod_backend
label_desc: Use image node_id 2 for calling a global function with example usage: apply_safety_factor(dtype) and returns: min_value,  max_value,  abs_smallest_val, int,  int,  None