code: def pca_reduce(X: tf.Tensor, latent_dim: tf.Tensor) -> tf.Tensor:
      """
      Linearly reduce the dimensionality of the input points `X` to `latent_dim` dimensions.
  
      :param X: Data to reduce.
      :param latent_dim: Number of latent dimension, Q < D.
      :return: PCA projection array.
  
      """
      if latent_dim > X.shape[1]:  # pragma: no cover
          raise ValueError(
              "Cannot have more latent dimensions than observed"
          )
      X_cov = tfp.stats.covariance(X)
      evals, evecs = tf.linalg.eigh(X_cov)
      W = evecs[:, -latent_dim:]
      return (X - tf.reduce_mean(X, axis=0, keepdims=True)) @ W
  
file_path: GPflow/gpflow/utilities/ops.py
imports: import copy
  from typing import Any, Callable, List, Optional
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes
label_desc: Use image node_id 6 for calling a global function with example usage: pca_reduce(X, latent_dim) and returns: unknown