code: def get_conv2d_weights_padding(N, K, tile_N, tile_K):
      """Compute the necessary padding for matrix B', where B'
      is the transformed version of matrix B in C=A*B.
  
      Parameters
      ----------
      N : int
          Number of columns in B = OC
      K : int
          Number of rows in B = KW * KH * IC
      tile_N : int
               tile size of B' on N axis
      tile_K : int
               tile size of B' on K axis
  
      Returns
      ----------
      pad_N : padding for N axis
      pad_K : padding for K axis
      """
      pad_N = 0
      pad_K = 0
  
      if N % tile_N != 0:
          pad_N = tile_N - (N % tile_N)
  
      # Tensorize will later make use of 4 tiles at once across the K axis so make sure we pad such
      # that K is multiple of 4
      K_multiplier = 4
      tile_K_multiplied = tile_K * K_multiplier
      K_misalignment = K % tile_K_multiplied
  
      if K_misalignment != 0:
          pad_K = tile_K_multiplied - K_misalignment
  
      return pad_N, pad_K
  
label_desc: Use image node_id 2 for calling a global function with example usage: get_conv2d_weights_padding(N, K, tile_N, tile_K) and returns: pad_N,  pad_K