code: def predict(self, x, mc=False, batch_size=32):
      # reshape 1dim input
      x = self._reshape_input(x)
  
      if not self.model_built:
          raise RuntimeError(
              "You must call fit_eval or restore first before calling predict!"
          )
      x = PytorchBaseModel.to_torch(x).float()
      if mc:
          self.model.train()
      else:
          self.model.eval()
      test_loader = DataLoader(
          TensorDataset(x), batch_size=int(batch_size)
      )
      y_list = []
      with torch.no_grad():
          for x_test_batch in test_loader:
              y_list.append(self.model(x_test_batch[0]).numpy())
      yhat = np.concatenate(y_list, axis=0)
      return yhat
  
imports: import torch
  from torch.utils.data import TensorDataset, DataLoader
  import types
  import numpy
  import math
  import pandas
  import tempfile
  import os
  from zoo.orca.automl.model.abstract import BaseModel, ModelBuilder
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.pytorch_utils import LR_NAME, DEFAULT_LR
label_desc: Use image node_id 15 for calling the PytorchBaseModel obj's underlying member method code with example usage: obj.predict(x, mc, batch_size) and returns: yhat