code: def check_consistent_length(*arrays, session=None, run_kwargs=None):
      """Check that all arrays have consistent first dimensions.
  
      Checks whether all objects in arrays have the same shape or length.
  
      Parameters
      ----------
      *arrays : list or tuple of input objects.
          Objects that will be checked for consistent length.
      """
  
      new_arrays = []
      lengths = []
      to_execute = []
      for X in arrays:
          if X is not None:
              n = _num_samples(X)
              if np.isnan(n):
                  to_execute.append(X)
              new_arrays.append(X)
              lengths.append(n)
      # unknown length exists
      if len(to_execute) > 0:
          # update shape
          ExecutableTuple(to_execute).execute(
              session=session, **(run_kwargs or dict())
          )
          # get length again
          lengths = [_num_samples(X) for X in new_arrays]
  
      uniques = np.unique(lengths)
      if len(uniques) > 1:
          raise ValueError(
              "Found input variables with inconsistent numbers of"
              f" samples: {[int(length) for length in lengths]}"
          )
  
imports: import warnings
  import numbers
  import numpy
  from numpy.core.numeric import ComplexWarning
  from ...None import dataframe
  from ...None import tensor
  from ...core import ExecutableTuple
  from ...lib.sparse import issparse
  from ...tensor import Tensor
  from .checks import check_non_negative_then_return_value, assert_all_finite, AssertAllFinite
label_desc: Use image node_id 2 for calling a global function with example usage: check_consistent_length() without return types