code: def convert_to_fp32(tensor):
      """
      Recursively converts the elements nested list/tuple/dictionary of tensors in FP16/BF16 precision to FP32.
  
      Args:
          tensor (nested list/tuple/dictionary of `torch.Tensor`):
              The data to convert from FP16/BF16 to FP32.
  
      Returns:
          The same data structure as `tensor` with all tensors that were in FP16/BF16 precision converted to FP32.
      """
  
      def _convert_to_fp32(tensor):
          return tensor.float()
  
      def _is_fp16_bf16_tensor(tensor):
          return hasattr(tensor, "dtype") and tensor.dtype in (
              torch.float16,
              torch.bfloat16,
          )
  
      return recursively_apply(
          _convert_to_fp32, tensor, test_type=_is_fp16_bf16_tensor
      )
  
imports: import pickle
  import warnings
  from functools import update_wrapper, wraps
  from typing import Any, Mapping
  import torch
  from ..state import PartialState
  from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES
  from .dataclasses import DistributedType, TensorInformation
  from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available
label_desc: Use image node_id 28 for calling a global function with example usage: convert_to_fp32(tensor) and returns: recursively_apply, tensor, unknown