code: def to_type(dtype, t):
      if isinstance(t, torch.Tensor):
          if not t.is_cuda:
              # This should not be a hard error, since it may be legitimate.
              warnings.warn("An input tensor was not cuda.")
          # GANs require this.
          # if t.requires_grad:
          #     warn_or_err("input data requires grad.  Since input data is not a model parameter,\n"
          #         "its gradients will not be properly allreduced by DDP.")
          if t.is_floating_point():
              return t.to(dtype)
          return t
      else:
          # Trust the user's custom batch type, that's all I can do here.
          return t.to(dtype)
  
imports: import collections.abc
  from types import MethodType
  import functools
  import sys
  import warnings
  import numpy
  import torch
  from ._amp_state import _amp_state, warn_or_err
  from .handle import disable_casts
  from .scaler import LossScaler
  from ._process_optimizer import _process_optimizer
  from apex.fp16_utils import convert_network
  from ..fp16_utils import FP16_Optimizer
  from ..contrib.optimizers import FP16_Optimizer
label_desc: Use image node_id 1 for calling a global function with example usage: to_type(dtype, t) and returns: t, t, t