code: def multigaussian_loss(
      preds, targets, ngauss=1
  ):  # pylint: disable=invalid-name
      """Returns a mixture of gaussians loss.
  
      Args:
        preds: <tbd>
        targets: <tbd>
        ngauss: <tbd>
      """
      ndims = targets.shape[-1]
      logits = preds[:, :ngauss]
      mus = preds[:, ngauss : ngauss * (ndims + 1)]
      sigmas = preds[:, ngauss(ndims + 1) :]
      sigmas = sigmas * sigmas + 1e-6  # Make positive.
      loglogits = logits - fastmath.logsumexp(
          logits, axis=-1, keepdims=True
      )
      mus = jnp.reshape(mus, [-1, ngauss, ndims])
      sigmas = jnp.reshape(sigmas, [-1, ngauss, ndims])
      targets = jnp.reshape(targets, [-1, 1, ndims])
      glogprobs = log_gaussian_diag_pdf(targets, mus, sigmas)
      return fastmath.logsumexp(loglogits + glogprobs, axis=-1)
  
imports: from absl import logging
  import numpy
  import tensorflow
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
label_desc: Use image node_id 19 for calling a global function with example usage: multigaussian_loss(preds, targets, ngauss) and returns: fastmath