code: def _normalize_sequence(array, value, fname):
      """
      values = _normalize_sequence(array, value, fname)
      If `value` is a sequence, checks that it has an element for each dimension
      of `array`. Otherwise, returns a sequence that repeats `value` once for
      each dimension of array.
      Parameters
      ----------
      array : Tensor
      value : sequence or scalar
      fname : str
          Function name. Used in error messages
      Returns
      -------
      values : sequence
      """
      try:
          value = list(value)
      except TypeError:
          return [value for s in array.shape]
  
      if len(value) != array.ndim:
          raise ValueError(
              f"caer.{fname}: argument is sequence, but has wrong size ({len(value)} "
              f"for an array of {array.ndim} dimensions)"
          )
      return value
  
file_path: caer/caer/_internal.py
imports: import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: _normalize_sequence(array, value, fname) and returns: value, unknown