code: def check_input_tuples(
      input_data, context, preprocessor, args_for_sk_checks, tuple_size
  ):
      preprocessor_has_been_applied = False
      if input_data.ndim == 2:
          if preprocessor is not None:
              input_data = preprocess_tuples(input_data, preprocessor)
              preprocessor_has_been_applied = True
          else:
              make_error_input(201, input_data, context)
      elif input_data.ndim == 3:
          pass
      else:
          if preprocessor is not None:
              make_error_input(420, input_data, context)
          else:
              make_error_input(200, input_data, context)
      input_data = check_array(
          input_data,
          allow_nd=True,
          ensure_2d=False,
          **args_for_sk_checks,
      )
      # we need to check num_features because check_array does not check it
      # for 3D inputs:
      if args_for_sk_checks["ensure_min_features"] > 0:
          n_features = input_data.shape[2]
          if n_features < args_for_sk_checks["ensure_min_features"]:
              raise ValueError(
                  "Found array with {} feature(s) (shape={}) while"
                  " a minimum of {} is required{}.".format(
                      n_features,
                      input_data.shape,
                      args_for_sk_checks["ensure_min_features"],
                      context,
                  )
              )
      #  normally we don't need to check_tuple_size too because tuple_size
      # shouldn't be able to be modified by any preprocessor
      if input_data.ndim != 3:
          # we have to ensure this because check_array above does not
          if preprocessor_has_been_applied:
              make_error_input(211, input_data, context)
          else:
              make_error_input(201, input_data, context)
      check_tuple_size(input_data, tuple_size, context)
      return input_data
  
imports: import numpy
  from numpy.linalg import LinAlgError
  from sklearn.datasets import make_spd_matrix
  from sklearn.decomposition import PCA
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_X_y, check_random_state
  from .exceptions import PreprocessorError, NonPSDError
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from scipy.linalg import pinvh, eigh
  import sys
  import time
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: check_input_tuples(input_data, context, preprocessor, args_for_sk_checks, tuple_size) and returns: input_data