code: def _determine_depth_max_pooling(
      x, kernel, strides, dims, data_format="channel_last"
  ):
      kernel, strides, depth_pooling = _depth_max_pooling_helper(
          x.shape, kernel, strides, dims=dims, data_format=data_format
      )
      if depth_pooling:
          x = np.transpose(x, (0, dims + 1, *range(1, dims + 1)))
      return x, kernel, strides, depth_pooling
  
imports: import math
  import numpy
  from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import ivy
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.backends.numpy.layers import _add_dilations
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode
  from ivy.func_wrapper import with_supported_dtypes
  from ivy.func_wrapper import with_unsupported_dtypes
  from .None import backend_version
label_desc: Use image node_id 1 for calling a global function with example usage: _determine_depth_max_pooling(x, kernel, strides, dims, data_format) and returns: x,  kernel,  strides,  depth_pooling