code: def pad(img, padding, fill=0, padding_mode="constant"):
      dim_size = img.ndim
      if not hasattr(padding, "__len__"):
          if dim_size == 2:
              trans_padding = ((padding, padding), (padding, padding))
          elif dim_size == 3:
              trans_padding = (
                  (0, 0),
                  (padding, padding),
                  (padding, padding),
              )
      elif len(padding) == 2:
          if dim_size == 2:
              trans_padding = (
                  (padding[1], padding[1]),
                  (padding[0], padding[0]),
              )
          elif dim_size == 3:
              trans_padding = (
                  (0, 0),
                  (padding[1], padding[1]),
                  (padding[0], padding[0]),
              )
      elif len(padding) == 4:
          if dim_size == 2:
              trans_padding = (
                  (padding[1], padding[3]),
                  (padding[0], padding[2]),
              )
          elif dim_size == 3:
              trans_padding = (
                  (0, 0),
                  (padding[1], padding[3]),
                  (padding[0], padding[2]),
              )
      else:
          raise ValueError(
              "padding can only be 1D with size 1, 2, 4 only"
          )
  
      if padding_mode in ["constant", "edge", "reflect", "symmetric"]:
          return ivy.pad(
              img,
              trans_padding,
              mode=padding_mode,
              constant_values=fill,
          )
      else:
          raise ValueError("Unsupported padding_mode")
  
file_path: ivy/ivy/functional/frontends/paddle/vision/transforms.py
imports: import ivy
  from ivy.func_wrapper import with_supported_dtypes, with_unsupported_device_and_dtypes
  from ..tensor.tensor import Tensor
  from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
label_desc: Use image node_id 10 for calling a global function with example usage: pad(img, padding, fill, padding_mode) and returns: ivy