code: def _modify_input_for_dct(signals, n=None):
      """Pad or trim the provided NumPy array's innermost axis to length n."""
      signal = np.array(signals)
      if n is None or n == signal.shape[-1]:
          signal_mod = signal
      elif n >= 1:
          signal_len = signal.shape[-1]
          if n <= signal_len:
              signal_mod = signal[..., 0:n]
          else:
              output_shape = list(signal.shape)
              output_shape[-1] = n
              signal_mod = np.zeros(output_shape)
              signal_mod[..., 0 : signal.shape[-1]] = signal
      if n:
          assert signal_mod.shape[-1] == n
      return signal_mod
  
imports: import importlib
  import itertools
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops.signal import dct_ops
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 2 for calling a global function with example usage: _modify_input_for_dct(signals, n) and returns: signal_mod