code: def general_pool(
      inputs,
      init,
      reduce_fn,
      window_shape,
      strides,
      padding,
      dim,
      dilation=1,
      ceil_mode=False,
      count_include_pad=False,
  ):
      # This function assumes that param validation is already done
      window_shape = tuple(window_shape)
      strides = (
          (1,) + strides + (1,) if len(strides) == dim else strides
      )
      dims = (
          (1,) + window_shape + (1,)
          if len(window_shape) == dim
          else window_shape
      )
      if isinstance(dilation, int):
          dilation = (1,) + (dilation,) * dim + (1,)
      else:
          dilation = (1,) + tuple(dilation) + (1,)
  
      is_single_input = False
      if inputs.ndim == len(dims) - 1:
          # add singleton batch dimension because lax.reduce_window always
          # needs a batch dimension.
          inputs = inputs[None]
          is_single_input = True
  
      assert inputs.ndim == len(
          dims
      ), f"len({inputs.shape}) != len({dims})"
  
      # shape of window after dilation
      new_window_shape = tuple(
          window_shape[i - 1]
          + (dilation[i] - 1) * (window_shape[i - 1] - 1)
          for i in range(1, len(dims) - 1)
      )
      (
          inputs,
          window_shape,
          strides,
          depth_pooling,
      ) = _determine_depth_max_pooling(
          inputs, window_shape, strides, dim, data_format="channel_last"
      )
      if not depth_pooling:
          # manually creating padding list
          if isinstance(padding, str):
              pad_list = _pad_str_to_list(
                  inputs, dims, padding, strides, new_window_shape
              )
          else:
              if isinstance(padding, int):
                  padding = [(padding,) * 2] * dim
              pad_list = [(0, 0)] + list(padding) + [(0, 0)]
  
          if ceil_mode:
              c = []
              for i in range(len(dims) - 2):
                  pad_list[i + 1], ceil = _padding_ceil_mode(
                      inputs.shape[i + 1],
                      new_window_shape[i],
                      pad_list[i + 1],
                      strides[i + 1],
                      True,
                  )
                  c.append(ceil)
  
          if count_include_pad:
              # manually pad inputs with 0 if ceil_mode is True
              # because they're not counted in average calculation
              if ceil_mode:
                  ceil = [(0, c[i]) for i in range(len(dims) - 2)]
                  for i in range(len(dims) - 2):
                      pad_list[i + 1] = (
                          pad_list[i + 1][0],
                          pad_list[i + 1][1] - ceil[i][1],
                      )
                  inputs = jnp.pad(
                      inputs,
                      pad_list,
                      mode="constant",
                      constant_values=1.0,
                  )
                  inputs = jnp.pad(
                      inputs,
                      [(0, 0)] + ceil + [(0, 0)],
                      mode="constant",
                      constant_values=0.0,
                  )
              else:
                  # manually pad inputs with 1s
                  # because they are counted in average calculation
                  inputs = jnp.pad(
                      inputs,
                      pad_list,
                      mode="constant",
                      constant_values=1.0,
                  )
              pad_list = [(0, 0)] * len(pad_list)
      elif isinstance(padding, list) and any(
          item != 0 for sublist in padding for item in sublist
      ):
          raise NotImplementedError(
              "Nonzero explicit padding is not supported for depthwise max pooling"
          )
      else:
          pad_list = [(0, 0)] * (dim + 2)
  
      if not ivy.is_array(inputs):
          # if dtype is not set here, jax casts it to float64
          inputs = jnp.array(inputs, dtype=jnp.float32)
      if not ivy.is_array(init):
          init = jnp.array(init, dtype=inputs.dtype)
      promoted_type = jnp.promote_types(inputs.dtype, init.dtype)
      inputs = inputs.astype(promoted_type)
      init = init.astype(promoted_type)
      y = jlax.reduce_window(
          inputs,
          init,
          reduce_fn,
          dims,
          strides,
          pad_list,
          window_dilation=dilation,
      )
      if is_single_input:
          y = jnp.squeeze(y, axis=0)
      return y
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import jax
  import jax.lax
  import jax.numpy
  import math
  import ivy
  from ivy import output_to_native_arrays
  from ivy.functional.backends.jax import JaxArray
  from ivy.functional.backends.jax.random import RNG
  from ivy.functional.ivy.experimental.general import _correct_ivy_callable
  from ivy.functional.ivy.layers import _handle_padding, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _get_size
  from ivy.func_wrapper import with_supported_dtypes
  from ivy.func_wrapper import with_unsupported_dtypes
  from .None import backend_version
  from ivy.functional.backends.jax.experimental.manipulation import _to_nested_tuple
label_desc: Use image node_id 3 for calling a global function with example usage: general_pool(inputs, init, reduce_fn, window_shape, strides, padding, dim, dilation, ceil_mode, count_include_pad) and returns: y