code: def broadcast_feature_to(array: jnp.ndarray, shape: jnp.shape):
      """Matches the shape dimensions (everything except the channel dims).
  
      This is useful when you watch to match the shape of two features that have
      a different number of channels.
  
      Args:
        array: the array to broadcast.
        shape: the shape to broadcast the tensor to.
  
      Returns:
        The broadcasted tensor.
      """
      out_shape = (*shape[:-1], array.shape[-1])
      return jnp.broadcast_to(array, out_shape)
  
file_path: nerfies/nerfies/model_utils.py
imports: from flax import linen
  from flax import optim
  from flax import struct
  from jax import lax
  from jax import random
  import jax.numpy
label_desc: Use image node_id 9 for calling a global function with example usage: broadcast_feature_to(array, shape) and returns: jnp