code: def calc_pad_sz(
      in_sz,
      out_sz,
      field_of_view,
      projected_grid,
      scale_factor,
      dim_by_convs,
      fw,
      device,
  ):
      if not dim_by_convs:
          # determine padding according to neighbor coords out of bound.
          # this is a generalized notion of padding, when pad<0 it means crop
          pad_sz = [
              -field_of_view[0, 0].item(),
              field_of_view[-1, -1].item() - in_sz + 1,
          ]
  
          # since input image will be changed by padding, coordinates of both
          # field_of_view and projected_grid need to be updated
          field_of_view += pad_sz[0]
          projected_grid += pad_sz[0]
  
      else:
          # only used for by_convs, to calc the boundaries of each filter the
          # number of distinct convolutions is the numerator of the scale factor
          num_convs, stride = (
              scale_factor.numerator,
              scale_factor.denominator,
          )
  
          # calculate left and right boundaries for each conv. left can also be
          # negative right can be bigger than in_sz. such cases imply padding if
          # needed. however if# both are in-bounds, it means we need to crop,
          # practically apply the conv only on part of the image.
          left_pads = -field_of_view[:, 0]
  
          # next calc is tricky, explanation by rows:
          # 1) counting output pixels between the first position of each filter
          #    to the right boundary of the input
          # 2) dividing it by number of filters to count how many 'jumps'
          #    each filter does
          # 3) multiplying by the stride gives us the distance over the input
          #    coords done by all these jumps for each filter
          # 4) to this distance we add the right boundary of the filter when
          #    placed in its leftmost position. so now we get the right boundary
          #    of that filter in input coord.
          # 5) the padding size needed is obtained by subtracting the rightmost
          #    input coordinate. if the result is positive padding is needed. if
          #    negative then negative padding means shaving off pixel columns.
          right_pads = (
              (
                  (out_sz - fw_arange(num_convs, fw, device) - 1)  # (1)
                  // num_convs
              )  # (2)
              * stride  # (3)
              + field_of_view[:, -1]  # (4)
              - in_sz
              + 1
          )  # (5)
  
          # in the by_convs case pad_sz is a list of left-right pairs. one per
          # each filter
  
          pad_sz = list(zip(left_pads, right_pads))
  
      return pad_sz, projected_grid, field_of_view
  
imports: import warnings
  from fractions import Fraction
  from math import ceil
  from typing import Tuple
  import disco_diffusion_clip_rn101.resize_right.interp_methods
label_desc: Use image node_id 4 for calling a global function with example usage: calc_pad_sz(in_sz, out_sz, field_of_view, projected_grid, scale_factor, dim_by_convs, fw, device) and returns: pad_sz,  projected_grid,  field_of_view