code: def _build_and_apply_h_projection(h, num_filters, dtype):
      """Project the conditional input."""
      h = tf.keras.layers.Flatten(dtype=dtype)(h)
      h_projection = tf.keras.layers.Dense(
          2 * num_filters,
          kernel_initializer="random_normal",
          dtype=dtype,
      )(h)
      return h_projection[..., tf.newaxis, tf.newaxis, :]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import warnings
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import quantized_distribution
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 2 for calling a global function with example usage: _build_and_apply_h_projection(h, num_filters, dtype) and returns: h_projection