code: def pad_or_trim_to_expected_length(
      vector, expected_len, pad_value=0, len_tolerance=20, use_tf=False
  ):
      """Make vector equal to the expected length.
  
      Feature extraction functions like `compute_loudness()` or `compute_f0` produce
      feature vectors that vary in length depending on factors such as `sample_rate`
      or `hop_size`. This function corrects vectors to the expected length, warning
      the user if the difference between the vector and expected length was
      unusually high to begin with.
  
      Args:
        vector: Numpy 1D ndarray. Shape [vector_length,]
        expected_len: Expected length of vector.
        pad_value: Value to pad at end of vector.
        len_tolerance: Tolerance of difference between original and desired vector
          length.
        use_tf: Make function differentiable by using tensorflow.
  
      Returns:
        vector: Vector with corrected length.
  
      Raises:
        ValueError: if `len(vector)` is different from `expected_len` beyond
        `len_tolerance` to begin with.
      """
      expected_len = int(expected_len)
      vector_len = int(vector.shape[-1])
  
      if abs(vector_len - expected_len) > len_tolerance:
          # Ensure vector was close to expected length to begin with
          raise ValueError(
              "Vector length: {} differs from expected length: {} "
              "beyond tolerance of : {}".format(
                  vector_len, expected_len, len_tolerance
              )
          )
      # Pick tensorflow or numpy.
      lib = tf if use_tf else np
  
      is_1d = len(vector.shape) == 1
      vector = vector[lib.newaxis, :] if is_1d else vector
  
      # Pad missing samples
      if vector_len < expected_len:
          n_padding = expected_len - vector_len
          vector = lib.pad(
              vector,
              ((0, 0), (0, n_padding)),
              mode="constant",
              constant_values=pad_value,
          )
      # Trim samples
      elif vector_len > expected_len:
          vector = vector[..., :expected_len]
  
      # Remove temporary batch dimension.
      vector = vector[0] if is_1d else vector
      return vector
  
imports: import crepe
  from ddsp import core
  from ddsp.core import safe_log
  from ddsp.core import tf_float32
  import gin
  import librosa
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
label_desc: Use image node_id 14 for calling a global function with example usage: pad_or_trim_to_expected_length(vector, expected_len, pad_value, len_tolerance, use_tf) and returns: vector