old_contents: # coding=utf-8
  # Copyright 2021 The Dopamine Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Various losses used by the Dopamine JAX agents."""
  from flax import linen as nn
  import jax.numpy as jnp
  
  
  def huber_loss(targets: jnp.array,
                 predictions: jnp.array,
                 delta: float = 1.0):
    """Implementation of the Huber loss with threshold delta.
  
    Let `x = |targets - predictions|`, the Huber loss is defined as:
    `0.5 * x^2` if `x <= delta`
    `0.5 * delta^2 + delta * (x - delta)` otherwise.
  
    Args:
      targets: Target values.
      predictions: Prediction values.
      delta: Threshold.
  
    Returns:
      Huber loss.
    """
    x = jnp.abs(targets - predictions)
    return jnp.where(x <= delta,
                     0.5 * x**2,
                     0.5 * delta**2 + delta * (x - delta))
  
  
  def mse_loss(targets: jnp.array,
               predictions: jnp.array):
    """Implementation of the mean squared error loss."""
    return jnp.power((targets - predictions), 2)
  
  
  def softmax_cross_entropy_loss_with_logits(labels: jnp.array,
                                             logits: jnp.array):
    """Implementation of the softmax cross entropy loss."""
    return -jnp.sum(labels * nn.log_softmax(logits))
  
new_contents: # coding=utf-8
  # Copyright 2021 The Dopamine Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Various losses used by the Dopamine JAX agents."""
  from flax import linen as nn
  import jax.numpy as jnp
  
  
  def huber_loss(targets: jnp.array,
                 predictions: jnp.array,
                 delta: float = 1.0) -> jnp.ndarray:
    """Implementation of the Huber loss with threshold delta.
  
    Let `x = |targets - predictions|`, the Huber loss is defined as:
    `0.5 * x^2` if `x <= delta`
    `0.5 * delta^2 + delta * (x - delta)` otherwise.
  
    Args:
      targets: Target values.
      predictions: Prediction values.
      delta: Threshold.
  
    Returns:
      Huber loss.
    """
    x = jnp.abs(targets - predictions)
    return jnp.where(x <= delta,
                     0.5 * x**2,
                     0.5 * delta**2 + delta * (x - delta))
  
  
  def mse_loss(targets: jnp.array, predictions: jnp.array) -> jnp.ndarray:
    """Implementation of the mean squared error loss."""
    return jnp.power((targets - predictions), 2)
  
  
  def softmax_cross_entropy_loss_with_logits(labels: jnp.array,
                                             logits: jnp.array) -> jnp.ndarray:
    """Implementation of the softmax cross entropy loss."""
    return -jnp.sum(labels * nn.log_softmax(logits))
  
subject: Add return type hints to losses.