code: def fit(self, x, y=None, epoch=1, batch_size=4):
      for e in range(epoch):
          self.model.train()
          if isinstance(x, DataLoader):
              size = len(x.dataset)
              for batch, (X, y) in enumerate(x):
                  loss = self.train_step(X, y)
                  current = batch * len(X)
                  if batch % 100 == 0:
                      logging.info(
                          f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]  \
                              epoch {e}/{epoch}"
                      )
                      self.loss_history.append(loss)
          elif isinstance(x, ndarray):
              i, size = 0, len(x)
              # just abandon last batch to reduce code
              while i < size:
                  end_index = (
                      i + batch_size if i + batch_size < size else size
                  )
                  X = torch.from_numpy(x[i:end_index])
                  Y = (
                      torch.from_numpy(y[i:end_index])
                      if y is not None
                      else None
                  )
                  loss = self.train_step(X, Y)
                  i += batch_size
                  if i % 100 == 0:
                      logging.info(
                          f"loss: {loss:>7f}  [{i:>5d}/{size:>5d}]  \
                              epoch {e}/{epoch}"
                      )
                      self.loss_history.append(loss)
          else:
              invalidInputError(
                  False, f"got unsupported data input type: {type(x)}"
              )
          if self.server_model_path is not None:
              self.save_server_model(self.server_model_path)
          if self.client_model_path is not None:
              torch.save(self.model, self.client_model_path)
  
imports: import logging
  from numpy import ndarray
  import torch
  from torch import nn
  from bigdl.ppml.fl.nn.fl_client import FLClient
  from torch.utils.data import DataLoader
  from bigdl.dllib.utils.log4Error import invalidInputError, invalidOperationError
  from bigdl.ppml.fl.nn.nn_client import NNClient
  from bigdl.ppml.fl.nn.utils import file_chunk_generate, tensor_map_to_ndarray_map
  import os
  import tempfile
  from nn_service_pb2 import LoadModelRequest, SaveModelRequest
label_desc: Use image node_id 7 for calling the PytorchEstimator obj's underlying member method code with example usage: obj.fit(x, y, epoch, batch_size) without return types