code: def fw_conv(input, filter, stride):
      # we want to apply 1d conv to any nd array. the way to do it is to reshape
      # the input to a 4D tensor. first two dims are singeletons, 3rd dim stores
      # all the spatial dims that we are not convolving along now. then we can
      # apply conv2d with a 1xK filter. This convolves the same way all the other
      # dims stored in the 3d dim. like depthwise conv over these.
      # TODO: numpy support
      reshaped_input = input.reshape(1, 1, -1, input.shape[-1])
      reshaped_output = paddle.nn.functional.conv2d(
          reshaped_input, filter.view(1, 1, 1, -1), stride=(1, stride)
      )
      return reshaped_output.reshape(*input.shape[:-1], -1)
  
imports: import warnings
  from fractions import Fraction
  from math import ceil
  from typing import Tuple
  import disco_diffusion_ernievil_base.resize_right.interp_methods
label_desc: Use image node_id 15 for calling a global function with example usage: fw_conv(input, filter, stride) and returns: reshaped_output