code: def set_floatx(value):
      """Sets the default float type.
  
      Note: It is not recommended to set this to float16 for training, as this will
      likely cause numeric stability issues. Instead, mixed precision, which is
      using a mix of float16 and float32, can be used by calling
      `tf.keras.mixed_precision.set_global_policy('mixed_float16')`. See the
      [mixed precision guide](
        https://www.tensorflow.org/guide/keras/mixed_precision) for details.
  
      Args:
          value: String; `'float16'`, `'float32'`, or `'float64'`.
  
      Example:
      >>> tf.keras.backend.floatx()
      'float32'
      >>> tf.keras.backend.set_floatx('float64')
      >>> tf.keras.backend.floatx()
      'float64'
      >>> tf.keras.backend.set_floatx('float32')
  
      Raises:
          ValueError: In case of invalid value.
      """
      global _FLOATX
      if value not in {"float16", "float32", "float64"}:
          raise ValueError("Unknown floatx type: " + str(value))
      _FLOATX = str(value)
  
imports: from tensorflow.python.util import dispatch
label_desc: Use image node_id 4 for calling a global function with example usage: set_floatx(value) without return types