class_docstr: DNN Model
  Parameters
  ----------
  input_dim : int
      input dimension
  output_dim : int
      output dimension
  layers : tuple
      layer sizes
  lr : float
      learning rate
  optimizer : str
      optimizer name
  GPU : int
      the GPU ID used for training
class_docstr_tok: ["DNN","Model","Parameters","--","--","--","--","--","input_dim",":","int","input","dimension","output_dim",":","int","output","dimension","layers",":","tuple","layer","sizes","lr",":","float","learning","rate","optimizer",":","str","optimizer","name","GPU",":","int","the","GPU","ID","used","for","training"]
docstr: Reusing predicting NN.
  Scenarios
  1) test inference (data may come from CPU and expect the output data is on CPU)
  2) evaluation on training (data may come from GPU)
code: def _nn_predict(self, data, return_cpu=True):
      """Reusing predicting NN.
      Scenarios
      1) test inference (data may come from CPU and expect the output data is on CPU)
      2) evaluation on training (data may come from GPU)
      """
      if not isinstance(data, torch.Tensor):
          if isinstance(data, pd.DataFrame):
              data = data.values
          data = torch.Tensor(data)
      data = data.to(self.device)
      preds = []
      self.dnn_model.eval()
      with torch.no_grad():
          batch_size = 8096
          for i in range(0, len(data), batch_size):
              x = data[i : i + batch_size]
              preds.append(
                  self.dnn_model(x.to(self.device)).detach().reshape(-1)
              )
      if return_cpu:
          preds = np.concatenate([pr.cpu().numpy() for pr in preds])
      else:
          preds = torch.cat(preds, axis=0)
      return preds
  
imports: from __future__ import division
  from __future__ import print_function
  from collections import defaultdict
  import os
  import gc
  import numpy
  import pandas
  from typing import Callable, Optional, Text, Union
  from sklearn.metrics import roc_auc_score, mean_squared_error
  import torch
  import torch.nn
  import torch.optim
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...data.dataset.weight import Reweighter
  from ...utils import auto_filter_kwargs, init_instance_by_config, unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path
  from ...log import get_module_logger
  from ...workflow import R
  from qlib.contrib.meta.data_selection.utils import ICLoss
  from torch.nn import DataParallel
label_desc: Use image node_id 7 for calling the DNNModelPytorch obj's underlying member method code with example usage: obj._nn_predict(data, return_cpu) and returns: preds