code: def _pad_before_conv(
      x, filters, strides, padding, dims, dilations, data_format
  ):
      dilations = _convert_to_list(dilations, dims, "dilations")
      strides = _convert_to_list(strides, dims, "strides")
  
      if isinstance(padding, str):
          # Case 1: "VALID", "SAME" etc.
          filter_shape = [
              filters.shape[i]
              + (filters.shape[i] - 1) * (dilations[i] - 1)
              for i in range(dims)
          ]
          padding_spec = [
              _handle_padding(
                  x.shape[1 + i], strides[i], filter_shape[i], padding
              )
              for i in range(dims - 1, -1, -1)
          ]
          padding_top = [padding_spec[i] // 2 for i in range(dims)]
          padding_bot = [
              padding_spec[i] - padding_spec[i] // 2
              for i in range(dims)
          ]
          padding = [None] * len(padding_top) * 2
          padding[::2] = padding_top
          padding[1::2] = padding_bot
  
      else:
          if isinstance(padding, int):
              padding = [(padding, padding)] * dims
          if (
              _is_list_or_tuple(padding)
              and len(padding) == dims
              and _is_list_or_tuple(padding[0])
          ):
              # Case 2: [(pad_left, pad_right), (pad_top, pad_bottom)...]
              padding = [
                  item for sublist in padding for item in sublist[::-1]
              ][::-1]
          else:
              raise ValueError(f"Invalid padding format: {padding}")
  
      if not all(p >= 0 for p in padding):
          raise ValueError(
              "Invalid padding, all values should be larger than"
              f"or equal to 0, but received: {padding}."
          )
  
      return paddle.nn.functional.pad(
          x, pad=padding, data_format=data_format, mode="constant"
      )
  
imports: from typing import Optional, Tuple, Union, Sequence
  import paddle
  import ivy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes
  from ivy.utils.exceptions import IvyNotImplementedException
  from ivy.functional.ivy.layers import _handle_padding, _get_x_data_format, _deconv_length
  import ivy.functional.backends.paddle
  from .None import backend_version
label_desc: Use image node_id 3 for calling a global function with example usage: _pad_before_conv(x, filters, strides, padding, dims, dilations, data_format) and returns: paddle