code: def get_projected_grid(
      in_sz, out_sz, scale_factor, fw, by_convs, device=None
  ):
      # we start by having the ouput coordinates which are just integer locations
      # in the special case when usin by_convs, we only need two cycles of grid
      # points. the first and last.
      grid_sz = out_sz if not by_convs else scale_factor.numerator
      out_coordinates = fw_arange(grid_sz, fw, device)
  
      # This is projecting the ouput pixel locations in 1d to the input tensor,
      # as non-integer locations.
      # the following fomrula is derived in the paper
      # "From Discrete to Continuous Convolutions" by Shocher et al.
      return (
          out_coordinates / float(scale_factor)
          + (in_sz - 1) / 2
          - (out_sz - 1) / (2 * float(scale_factor))
      )
  
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 2 for calling a global function with example usage: get_projected_grid(in_sz, out_sz, scale_factor, fw, by_convs, device) and returns: unknown