code: def test_normalize(input_shape):
      mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
      transfo = T.Normalize(mean, std)
      input_t = tf.cast(tf.fill(input_shape, 1), dtype=tf.float32)
  
      out = transfo(input_t)
  
      assert tf.reduce_all(out == 1)
      assert repr(transfo) == f"Normalize(mean={mean}, std={std})"
  
      # FP16
      input_t = tf.cast(tf.fill(input_shape, 1), dtype=tf.float16)
      out = transfo(input_t)
      assert out.dtype == tf.float16
  
imports: import math
  import numpy
  import pytest
  import tensorflow
  from doctr import transforms
  from doctr.transforms.functional import crop_detection, rotate_sample
label_desc: Use image node_id 3 for calling a global function with example usage: test_normalize(input_shape) without return types