code: def _get_new_padding_before_conv(
      x,
      filters,
      strides,
      padding,
      dims,
      data_format,
      filter_format,
      dilations,
      x_dilations,
  ):
      if len(x_dilations) != x_dilations.count(1):
          new_pad = [0] * dims
          x_shape = (
              list(x.shape[1 : dims + 1])
              if data_format == ("NWC" or "NHWC" or "NDHWC")
              else list(x.shape[2:])
          )
          x_shape = [
              x_shape[i] + (x_shape[i] - 1) * (x_dilations[i] - 1)
              for i in range(dims)
          ]
          f_shape = (
              list(filters.shape[:dims])
              if filter_format == "channel_last"
              else list(filters.shape[2:])
          )
          f_shape = [
              f_shape[i] + (f_shape[i] - 1) * (dilations[i] - 1)
              for i in range(dims)
          ]
          if isinstance(padding, str):
              for i in range(dims):
                  new_pad[i] = _handle_padding(
                      x_shape[i], strides[i], f_shape[i], padding
                  )
              padding = [
                  (new_pad[i] // 2, new_pad[i] - new_pad[i] // 2)
                  for i in range(dims)
              ]
          return padding
      return padding
  
file_path: ivy/ivy/functional/backends/jax/layers.py
imports: import jax.lax
  import jax.numpy
  import ivy
  from ivy.functional.backends.jax import JaxArray
  from typing import Union, Tuple, Optional, Sequence
  from ivy.functional.ivy.layers import _handle_padding, _deconv_length, _get_x_data_format
label_desc: Use image node_id 3 for calling a global function with example usage: _get_new_padding_before_conv(x, filters, strides, padding, dims, data_format, filter_format, dilations, x_dilations) and returns: padding, padding