code: def perform_actions_initialization(
      shape, axes, input_shape, input_rank_tensor
  ):
      perform_padding = shape is not None
      perform_transpose = tf.math.logical_not(
          tf.math.reduce_all(
              tf.math.equal(
                  axes,
                  tf.range(
                      input_rank_tensor - tf.size(axes),
                      input_rank_tensor,
                  ),
              )
          )
      )
      return perform_padding, perform_transpose
  
imports: import math
  from typing import Union, Optional, Tuple, List, Literal, Sequence, Callable
  import tensorflow
  from ivy.func_wrapper import inputs_to_ivy_arrays, output_to_native_arrays, with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  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.ivy.experimental.layers import _padding_ceil_mode, _get_size
label_desc: Use image node_id 30 for calling a global function with example usage: perform_actions_initialization(shape, axes, input_shape, input_rank_tensor) and returns: perform_padding,  perform_transpose