code: def factorization(
      dimension: int, factor: int = -1
  ) -> tuple[int, int]:
      """
      return a tuple of two value of input dimension decomposed by the number closest to factor
      second value is higher or equal than first value.
  
      In LoRA with Kroneckor Product, first value is a value for weight scale.
      secon value is a value for weight.
  
      Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
  
      examples)
      factor
          -1               2                4               8               16               ...
      127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
      128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
      250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
      360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
      512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
      1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
      """
  
      if factor > 0 and (dimension % factor) == 0:
          m = factor
          n = dimension // factor
          if m > n:
              n, m = m, n
          return m, n
      if factor < 0:
          factor = dimension
      m, n = 1, dimension
      length = m + n
      while m < n:
          new_m = m + 1
          while dimension % new_m != 0:
              new_m += 1
          new_n = dimension // new_m
          if new_m + new_n > length or new_m > factor:
              break
          else:
              m, n = new_m, new_n
      if m > n:
          n, m = m, n
      return m, n
  
imports: import torch
label_desc: Use image node_id 4 for calling a global function with example usage: factorization(dimension, factor) and returns: m,  n, m,  n