code: def _create_rdd_x(x, input_names, sc):
      from tensorflow.python.keras.engine import training_utils
  
      x = training_utils.standardize_input_data(
          x,
          input_names,
          check_batch_axis=False,
          exception_prefix="input",
      )
  
      num_samples = x[0].shape[0]
      num_inputs = len(x)
  
      input_data = []
      for i in range(num_samples):
          sample = []
          for j in range(num_inputs):
              sample.append(x[j][i])
  
          input_data.append(sample)
  
      types = [x.dtype for x in input_data[0]]
      shapes = [x.shape for x in input_data[0]]
  
      rdd = sc.parallelize(input_data)
      return rdd, types, shapes
  
imports: import numpy
  from bigdl.dllib.optim.optimizer import MaxEpoch
  from bigdl.orca.tfpark.utils import evaluate_string_metrics
  from bigdl.dllib.utils.file_utils import enable_multi_fs_save, enable_multi_fs_load
  from bigdl.dllib.nncontext import getOrCreateSparkContext
  from bigdl.orca.tfpark.tf_dataset import TFNdarrayDataset, TFDataset, _standarize_feature_label_dataset, check_data_compatible
  from bigdl.orca.tfpark.tf_optimizer import TFOptimizer
  from bigdl.orca.tfpark.tf_predictor import TFPredictor
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 2 for calling a global function with example usage: _create_rdd_x(x, input_names, sc) and returns: rdd,  types,  shapes