code: def dwconv1d_fold(x, w, bias=None, padding=0, dilation=1):
      """Assumes that output shape is the same as input shape"""
      # Force NWC format
      x_folded = rearrange(
          rearrange(
              F.pad(x, (padding, padding)),
              "b c (w d) -> (b d) w c",
              d=dilation,
          ),
          "b w c -> b c w",
      )
      out = rearrange(
          F.conv1d(x_folded, w, bias, groups=w.shape[0]),
          "(b d) c w -> b c (w d)",
          b=x.shape[0],
      )
      return out
  
file_path: zoo/src/ops/dwconv.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.cuda.amp import custom_bwd, custom_fwd
  from einops import rearrange
label_desc: Use image node_id 3 for calling a global function with example usage: dwconv1d_fold(x, w, bias, padding, dilation) and returns: out