class_docstr: Compute `Y = g(X) = DCT(X)`, where DCT type is indicated by the `type` arg.
  
  The [discrete cosine transform](
  https://en.wikipedia.org/wiki/Discrete_cosine_transform) efficiently applies
  a unitary DCT operator. This can be useful for mixing and decorrelating across
  the innermost event dimension.
  
  The inverse `X = g^{-1}(Y) = IDCT(Y)`, where IDCT is DCT-III for type==2.
  
  This bijector can be interleaved with affine bijectors to build a cascade of
  structured efficient linear layers as in [1].
  
  Note that the operator applied is orthonormal (i.e. `norm='ortho'`).
  
  #### References
  
  [1]: Moczulski M, Denil M, Appleyard J, de Freitas N. ACDC: A structured
       efficient linear layer. In _International Conference on Learning
       Representations_, 2016. https://arxiv.org/abs/1511.05946
code: def _forward_log_det_jacobian(self, x):
      return tf.constant(0.0, dtype=dtype_util.base_dtype(x.dtype))
  
file_path: probability/tensorflow_probability/python/bijectors/discrete_cosine_transform.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 6 for calling the DiscreteCosineTransform obj's underlying member method code with example usage: obj._forward_log_det_jacobian(x) and returns: tf