class_docstr: General N-dimensional downsampling over a sliding window.
class_docstr_tok: ["General","N-dimensional","downsampling","over","a","sliding","window","."]
code: def _update_padding_for_ceil(self, input_shape):
      new_padding = []
      for (
          input_size,
          (left_padding, right_padding),
          kernel_size,
          stride,
      ) in zip(
          input_shape[1:], self.padding, self.kernel_size, self.stride
      ):
          if (
              input_size + left_padding + right_padding - kernel_size
          ) % stride == 0:
              new_padding.append((left_padding, right_padding))
          else:
              new_padding.append((left_padding, right_padding + stride))
      return tuple(new_padding)
  
imports: import math
  from collections.abc import Callable, Sequence
  from typing import Optional, Union
  import jax
  import jax.lax
  import jax.numpy
  import jax.random
  from jaxtyping import Array, PRNGKeyArray
  from .._module import field, Module
  from ._misc import all_sequences
label_desc: Use image node_id 2 for calling the Pool obj's underlying member method code with example usage: obj._update_padding_for_ceil(input_shape) and returns: tuple