code: def _log1p_pade_4_4_fwd(x):
      x = tf.convert_to_tensor(x, dtype_hint=tf.float32)
      dtype = dtype_util.as_numpy_dtype(x.dtype)
      coeffs_p = np.array([5 / 84, 13 / 21, 3 / 2, 1, 0], dtype)
      coeffs_q = np.array([1 / 70, 2 / 7, 9 / 7, 2, 1], dtype)
      for_large_x = log_pade_4_4(1 + x)
      for_small_x = _horner(x, coeffs_p) / _horner(x, coeffs_q)
      x_is_small = tf.abs(x) < 0.7
      return tf.where(x_is_small, for_small_x, for_large_x), x
  
imports: import contextlib
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import dtype_util
label_desc: Use image node_id 19 for calling a global function with example usage: _log1p_pade_4_4_fwd(x) and returns: tf,  x