code: def norm_initialization(norm, shape, x):
      if norm == "backward":
          norm_factor = tf.constant(1, x.dtype)
      elif norm in ["forward", "ortho"]:
          norm_factor = tf.cast(tf.math.reduce_prod(shape), x.dtype)
          if norm == "ortho":
              norm_factor = tf.math.sqrt(norm_factor)
      return norm_factor
  
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 33 for calling a global function with example usage: norm_initialization(norm, shape, x) and returns: norm_factor