code: def compute_clipped_gradients_and_outputs(
      input_model: tf.keras.Model,
      l2_norm_clip: float,
      layer_registry: lr.LayerRegistry,
      x_batch: type_aliases.InputTensors,
      y_batch: tf.Tensor,
      weight_batch: Optional[tf.Tensor] = None,
      num_microbatches: Optional[type_aliases.BatchSize] = None,
      clipping_loss: Optional[type_aliases.LossFn] = None,
  ) -> tuple[Sequence[tf.Tensor], tf.Tensor, tf.Tensor]:
      """Computes the per-example clipped loss gradient and other useful outputs.
  
      Given a batch of observations `(x_batch, y_batch, weight_batch)`, the main
      steps of this function are:
      (i) compute the l2-norm of the gradients w.r.t. the trainable variables of
      `input_model`, for each weighted example loss in the batch;
      (ii) use the norms computed in (i) to obtain "clip_weights" that are used to
      reweight the loss function, such that each gradient of this reweighted loss
      has l2-norm at most `l2_norm_clip`.
  
      Args:
        input_model: The `tf.keras.Model` from which to obtain the layers from.
        l2_norm_clip: A `float` indicating the norm to which per-example gradients
          will be clipped. That is, all gradients of the per-example loss functions
          will have norm at most `l2_norm_clip`.
        layer_registry: A `dict` of layers that support "fast" gradient norm
          computations. The key is the class of the layer and the value is a
          function that returns a `tuple` `(output, sqr_grad_norms, vars)`, where
          `output` is the pre-activator tensor, `sqr_grad_norms` is related to the
          squared norms of a layer's pre-activation tensor, and `vars` are relevant
          trainable weights (see `layer_registry_factories.py` for examples).
        x_batch: An `InputTensor` representing a batch of inputs to the model. The
          first axis must be the batch dimension.
        y_batch: A `tf.Tensor` representing a batch of output labels. The first axis
          must be the batch dimension. The number of examples should match the
          number of examples in `x_batch`.
        weight_batch: Optional vector of weights, passed to the loss function. Must
          be of size [batch_size]. In case of microbatching, this will be reshaped
          to [num_microbatches, batch_size/num_microbatches] before passing it to
          the loss.
        num_microbatches: An optional number or scalar `tf.Tensor` for the number of
          microbatches. If not None, indicates that the loss is grouped into
          num_microbatches (in this case, the batch dimension needs to be a multiple
          of num_microbatches).
        clipping_loss: If provided, used for the clipping computation. Defaults to
          `input_model.compiled_loss`. Specifying a `clipping_loss` can be useful to
          avoid calling `input_model.compiled_loss`, as this will append the value
          of the clipped loss to the reported metrics, and this can be misleading as
          the value of the clipped loss does not reflect the true loss.
  
      Returns:
        clipped_grad: list of the clipped gradients of the loss function (one per
          trainable variable in `input_model`).
        y_pred: the result of applying `input_model` to `x_batch`.
        clipping_loss_value: the loss value weighted in such a way that its gradient
          is `clipped_grad`.
      """
      if input_model.loss.reduction == "none":
          raise NotImplementedError(
              "Fast gradient clipping does not support "
              "models with unreduced loss functions."
          )
      if clipping_loss is None:
          clipping_loss = input_model.compiled_loss
      gradient_norms = compute_gradient_norms(
          input_model,
          layer_registry,
          x_batch,
          y_batch,
          weight_batch,
          num_microbatches=num_microbatches,
          trainable_vars=input_model.trainable_variables,
      )
      clip_weights = compute_clip_weights(l2_norm_clip, gradient_norms)
      if weight_batch is not None:
          # Let w be the `weight_batch`, c be the `clip_weights`, and l be the losses.
          # c is computed based on the gradient of w*l, so that if we scale w*l by c,
          # the result has bounded per-example gradients. So the loss to optimize is
          # c*w*l. Here we compute c*w before passing it to the loss.
          weight_batch = common_manip_utils.maybe_add_microbatch_axis(
              weight_batch, num_microbatches
          )
          if num_microbatches is None:
              clip_weights = (
                  clip_weights * weight_batch
              )  # shape [num_microbatches]
          else:
              # In this case, weight_batch is of shape [batch_size, microbatch_size],
              # we multiply by the clip_weights (which is of shape [num_microbatches])
              clip_weights = clip_weights[:, tf.newaxis] * weight_batch
      with tf.GradientTape() as tape:
          # WARNING: When num_microbatches is not None, we need to be sure that
          # `compute_loss` always computes the mean over the microbatches
          # as it is the assumption made when computing the gradient norm.
          # It is indeed the case for multiple keras loss functions
          # (e.g. mean_squared_error and binary_crossentropy). However it
          # is not defined in the contract so may not hold, especially for
          # custom losses.
          y_pred = input_model(x_batch, training=True)
          mb_y_batch = common_manip_utils.maybe_add_microbatch_axis(
              y_batch, num_microbatches
          )
          mb_y_pred = common_manip_utils.maybe_add_microbatch_axis(
              y_pred, num_microbatches
          )
          clipping_loss_value = clipping_loss(
              mb_y_batch, mb_y_pred, clip_weights
          )
      clipped_grads = tape.gradient(
          clipping_loss_value,
          input_model.trainable_variables,
          unconnected_gradients=tf.UnconnectedGradients.ZERO,
      )
      return clipped_grads, y_pred, clipping_loss_value
  
file_path: privacy/tensorflow_privacy/privacy/fast_gradient_clipping/clip_grads.py
imports: from collections.abc import Sequence
  from typing import Optional
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import common_manip_utils
  from tensorflow_privacy.privacy.fast_gradient_clipping import gradient_clipping_utils
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
label_desc: Use image node_id 4 for calling a global function with example usage: compute_clipped_gradients_and_outputs(input_model, l2_norm_clip, layer_registry, x_batch, y_batch, weight_batch, num_microbatches, clipping_loss) and returns: clipped_grads,  y_pred,  clipping_loss_value