old_contents: # Copyright 2018 DeepMind Technologies Limited. All rights reserved.
  #
  # 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.
  
  """Base logger."""
  
  import abc
  from typing import Any, Mapping
  
  import numpy as np
  import tree
  
  LoggingData = Mapping[str, Any]
  
  
  class Logger(abc.ABC):
    """A logger has a `write` method."""
  
    @abc.abstractmethod
    def write(self, data: LoggingData) -> None:
      """Writes `data` to destination (file, terminal, database, etc)."""
  
    @abc.abstractmethod
    def close(self) -> None:
      """Closes the logger, not expecting any further write."""
  
  
  class NoOpLogger(Logger):
    """Simple Logger which does nothing and outputs no logs.
  
    This should be used sparingly, but it can prove useful if we want to quiet an
    individual component and have it produce no logging whatsoever.
    """
  
    def write(self, data: LoggingData):
      pass
  
  
  def tensor_to_numpy(value: Any):
    if hasattr(value, 'numpy'):
      return value.numpy()  # tf.Tensor (TF2).
    if hasattr(value, 'device_buffer'):
      return np.asarray(value)  # jnp.DeviceArray.
    return value
  
  
  def to_numpy(values: Any):
    """Converts tensors in a nested structure to numpy.
  
    Converts tensors from TensorFlow to Numpy if needed without importing TF
    dependency.
  
    Args:
      values: nested structure with numpy and / or TF tensors.
  
    Returns:
      Same nested structure as values, but with numpy tensors.
    """
    return tree.map_structure(tensor_to_numpy, values)
  
new_contents: # Copyright 2018 DeepMind Technologies Limited. All rights reserved.
  #
  # 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.
  
  """Base logger."""
  
  import abc
  from typing import Any, Mapping
  
  import numpy as np
  import tree
  
  LoggingData = Mapping[str, Any]
  
  
  class Logger(abc.ABC):
    """A logger has a `write` method."""
  
    @abc.abstractmethod
    def write(self, data: LoggingData) -> None:
      """Writes `data` to destination (file, terminal, database, etc)."""
  
    @abc.abstractmethod
    def close(self) -> None:
      """Closes the logger, not expecting any further write."""
  
  
  class NoOpLogger(Logger):
    """Simple Logger which does nothing and outputs no logs.
  
    This should be used sparingly, but it can prove useful if we want to quiet an
    individual component and have it produce no logging whatsoever.
    """
  
    def write(self, data: LoggingData):
      pass
  
    def close(self):
      pass
  
  
  def tensor_to_numpy(value: Any):
    if hasattr(value, 'numpy'):
      return value.numpy()  # tf.Tensor (TF2).
    if hasattr(value, 'device_buffer'):
      return np.asarray(value)  # jnp.DeviceArray.
    return value
  
  
  def to_numpy(values: Any):
    """Converts tensors in a nested structure to numpy.
  
    Converts tensors from TensorFlow to Numpy if needed without importing TF
    dependency.
  
    Args:
      values: nested structure with numpy and / or TF tensors.
  
    Returns:
      Same nested structure as values, but with numpy tensors.
    """
    return tree.map_structure(tensor_to_numpy, values)
  
subject: Add a missing close method.