code: def _validate_dct_arguments(input_tensor, dct_type, n, axis, norm):
      """Checks that DCT/IDCT arguments are compatible and well formed."""
      if axis != -1:
          raise NotImplementedError("axis must be -1. Got: %s" % axis)
      if n is not None and n < 1:
          raise ValueError("n should be a positive integer or None")
      if dct_type not in (1, 2, 3, 4):
          raise ValueError(
              "Types I, II, III and IV (I)DCT are supported."
          )
      if dct_type == 1:
          if norm == "ortho":
              raise ValueError(
                  "Normalization is not supported for the Type-I DCT."
              )
          if (
              input_tensor.shape[-1] is not None
              and input_tensor.shape[-1] < 2
          ):
              raise ValueError(
                  "Type-I DCT requires the dimension to be greater than one."
              )
  
      if norm not in (None, "ortho"):
          raise ValueError(
              "Unknown normalization. Expected None or 'ortho', got: %s"
              % norm
          )
  
file_path: tensorflow/tensorflow/python/ops/signal/dct_ops.py
imports: import math
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import smart_cond
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.signal import fft_ops
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: _validate_dct_arguments(input_tensor, dct_type, n, axis, norm) without return types