code: def float16_activations_var_getter(getter, *args, **kwargs):
    
    requested_dtype = kwargs["dtype"]
  
    if requested_dtype == tf.float16:
      kwargs["dtype"] = tf.float32
  
    if requested_dtype == tf.float32:
      requested_dtype = tf.float16
    var = getter(*args, **kwargs)
    # This if statement is needed to guard the cast, because batch norm
    # assigns directly to the return value of this custom getter. The cast
    # makes the return value not a variable so it cannot be assigned. Batch
    # norm variables are always in fp32 so this if statement is never
    # triggered for them.
    if var.dtype.base_dtype != requested_dtype:
      var = tf.cast(var, requested_dtype)
    return var
docs: A custom getter function for float32 parameters and float16 activations.
  
    This function ensures the following:
      1. All variables requested with type fp16 are stored as type fp32.
      2. All variables requested with type fp32 are returned as type fp16.
    See https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/
    #training_tensorflow for more information on this strategy.
  
    Args:
      getter: custom getter
      *args: arguments
      **kwargs: keyword arguments
  
    Returns:
      variables with the correct dtype.
  
    Raises:
      KeyError: if "dtype" is not provided as a kwarg.