code: def test_pad_or_trim_vector_to_expected_length(
      self, use_tf, num_dims
  ):
      vector_len = 10
      padded_vector_expected_len = 15
      trimmed_vector_expected_len = 4
  
      # Generate target vectors for testing
      vector = np.ones(vector_len) + np.random.uniform()
      num_pad = padded_vector_expected_len - vector_len
      target_padded = np.concatenate([vector, np.zeros(num_pad)])
      target_trimmed = vector[:trimmed_vector_expected_len]
  
      # Make a batch of target vectors
      if num_dims > 1:
          batch_size = 16
          vector = np.tile(vector, (batch_size, 1))
          target_padded = np.tile(target_padded, (batch_size, 1))
          target_trimmed = np.tile(target_trimmed, (batch_size, 1))
  
      vector_padded = spectral_ops.pad_or_trim_to_expected_length(
          vector, padded_vector_expected_len, use_tf=use_tf
      )
      vector_trimmmed = spectral_ops.pad_or_trim_to_expected_length(
          vector, trimmed_vector_expected_len, use_tf=use_tf
      )
      self.assertAllClose(target_padded, vector_padded)
      self.assertAllClose(target_trimmed, vector_trimmmed)
  
imports: from absl.testing import parameterized
  from ddsp import spectral_ops
  from ddsp.test_util import gen_np_sinusoid
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 1 for calling the PadOrTrimVectorToExpectedLengthTest obj's underlying member method code with example usage: obj.test_pad_or_trim_vector_to_expected_length(use_tf, num_dims) without return types