code: def compute_padding_1d_pytorch(
      pad_type, in_size, stride, filt_size, dilation
  ):
      if isinstance(pad_type, str):
          if pad_type.lower() == "same":
              n_out = int(
                  math.floor(
                      (
                          float(in_size)
                          + 2
                          - float(dilation) * (float(filt_size) - 1)
                          - 1
                      )
                      / float(stride)
                      + 1
                  )
              )  # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
              if in_size % stride == 0:
                  pad_along_size = max(filt_size - stride, 0)
              else:
                  pad_along_size = max(
                      filt_size - (in_size % stride), 0
                  )
              pad_right = pad_along_size // 2
              pad_left = pad_along_size - pad_right
          elif pad_type.lower() == "valid":
              n_out = int(
                  math.floor(
                      (
                          float(in_size)
                          - float(dilation) * (float(filt_size) - 1)
                          - 1
                      )
                      / float(stride)
                      + 1
                  )
              )  # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
              pad_left = 0
              pad_right = 0
          else:
              raise Exception(f"Unknown padding type: {pad_type}")
      else:
          if pad_type > 0:
              n_out = int(
                  math.floor(
                      (
                          float(in_size)
                          + 2 * pad_type
                          - float(dilation) * (float(filt_size) - 1)
                          - 1
                      )
                      / float(stride)
                      + 1
                  )
              )  # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
              pad_right = pad_type
              pad_left = pad_type
          else:
              n_out = int(
                  math.floor(
                      (
                          float(in_size)
                          - float(dilation) * (float(filt_size) - 1)
                          - 1
                      )
                      / float(stride)
                      + 1
                  )
              )  # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
              pad_left = 0
              pad_right = 0
  
      return (n_out, pad_left, pad_right)
  
imports: import math
label_desc: Use image node_id 4 for calling a global function with example usage: compute_padding_1d_pytorch(pad_type, in_size, stride, filt_size, dilation) and returns: n_out,  pad_left,  pad_right