code: def new_synthesize_flattened_rnn_weights(
      fp32_weights, fp16_flat_tensor, rnn_fn="", verbose=False
  ):
      fp16_weights = []
      fp32_base_ptr = fp32_weights[0].data_ptr()
      for w_fp32 in fp32_weights:
          w_fp16 = w_fp32.new().half()
          offset = (
              w_fp32.data_ptr() - fp32_base_ptr
          ) // w_fp32.element_size()
          w_fp16.set_(fp16_flat_tensor.storage(), offset, w_fp32.shape)
          w_fp16.copy_(w_fp32)
          if verbose:
              print("Float->Half ({})".format(rnn_fn))
          fp16_weights.append(w_fp16)
      return fp16_weights
  
imports: from .None import compat
  import functools
  import itertools
  import torch
label_desc: Use image node_id 19 for calling a global function with example usage: new_synthesize_flattened_rnn_weights(fp32_weights, fp16_flat_tensor, rnn_fn, verbose) and returns: fp16_weights