code: def _check_input(self, x, y):
      input_feature_num = x.shape[-1]
      input_output_dim = y.shape[-1]
      from bigdl.nano.utils.common import invalidInputError
  
      if input_feature_num is None:
          invalidInputError(False, "input x is None!")
      if input_output_dim is None:
          invalidInputError(False, "input y is None!")
  
      if (
          self.feature_num is not None
          and self.feature_num != input_feature_num
      ):
          invalidInputError(
              False,
              "input x has different feature number (the shape of last dimension) "
              "{} with the fitted model, which is {}.".format(
                  input_feature_num, self.feature_num
              ),
          )
      if (
          self.output_dim is not None
          and self.output_dim != input_output_dim
      ):
          invalidInputError(
              False,
              "input y has different prediction size (the shape of last dimension)"
              " of {} with the fitted model, which is {}.".format(
                  input_output_dim, self.output_dim
              ),
          )
      return input_feature_num, input_output_dim
  
imports: import pickle
  import numpy
  import time
  from tensorflow.keras.models import Model
  from tensorflow.keras.layers import Reshape, Dense, Dropout, Add, Permute, RNN, GRUCell
  from tensorflow.keras.layers import Input, InputSpec, Wrapper, Lambda, Conv2D, multiply, Softmax
  from tensorflow.keras.initializers import TruncatedNormal, Constant
  import tensorflow.keras.backend
  import tensorflow
  from bigdl.chronos.metric.forecast_metrics import Evaluator
label_desc: Use image node_id 9 for calling the MTNetKeras obj's underlying member method code with example usage: obj._check_input(x, y) and returns: input_feature_num,  input_output_dim