old_contents: # Copyright 2020 The TensorFlow Authors. 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.
  # ============================================================================
  """Ops to manipulate hashmap of tensors."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  # go/tf-wildcard-import
  # pylint: disable=wildcard-import
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_map_ops
  from tensorflow.python.ops.gen_map_ops import *
  
  ops.NotDifferentiable("EmptyTensorMap")
  
  
  def empty_tensor_map():
    return gen_map_ops.empty_tensor_map()
  
  
  def tensor_map_size(input_handle):
    return gen_map_ops.tensor_map_size(input_handle)
  
  
  def tensor_map_insert(input_handle, key, value):
    return gen_map_ops.tensor_map_insert(input_handle, key, value)
  
  
  def tensor_map_lookup(input_handle, key, value_dtype):
    return gen_map_ops.tensor_map_lookup(input_handle, key, value_dtype)
  
  
  def tensor_map_erase(input_handle, key, value_dtype):
    m = gen_map_ops.tensor_map_erase(input_handle, key, value_dtype)
    return m
  
  
  def tensor_map_has_key(input_handle, key):
    return gen_map_ops.tensor_map_has_key(input_handle, key)
  
  
  @ops.RegisterGradient("TensorMapLookup")
  def LookupGrad(op, dval):
    _, k = op.inputs
    map_grad = empty_tensor_map()
    map_grad = tensor_map_insert(map_grad, k, dval)
    key_grad = None
    return map_grad, key_grad
  
  
  @ops.RegisterGradient("TensorMapInsert")
  def InsertGrad(op, dmap):
    _, k, v = op.inputs
    key_grad = None
    (value_grad, map_grad) = control_flow_ops.cond(tensor_map_has_key(dmap, k),
                                                   lambda: (tensor_map_lookup(dmap, k, v.dtype), tensor_map_erase(dmap, k, v.dtype)),
                                                   lambda: (array_ops.zeros_like(v), dmap))
    return map_grad, key_grad, value_grad
  
  @ops.RegisterGradient("TensorMapErase")
  def EraseGrad(op, dmap):
    m, k = op.inputs
    key_grad = None
    map_grad = dmap #tensor_map_insert(dmap, k, v)
    return map_grad, key_grad
  
new_contents: # Copyright 2020 The TensorFlow Authors. 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.
  # ============================================================================
  """Ops to manipulate hashmap of tensors."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  # go/tf-wildcard-import
  # pylint: disable=wildcard-import
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_map_ops
  from tensorflow.python.ops.gen_map_ops import *
  
  ops.NotDifferentiable("EmptyTensorMap")
  
  
  def empty_tensor_map():
    return gen_map_ops.empty_tensor_map()
  
  
  def tensor_map_size(input_handle):
    return gen_map_ops.tensor_map_size(input_handle)
  
  
  def tensor_map_insert(input_handle, key, value):
    return gen_map_ops.tensor_map_insert(input_handle, key, value)
  
  
  def tensor_map_lookup(input_handle, key, value_dtype):
    return gen_map_ops.tensor_map_lookup(input_handle, key, value_dtype)
  
  
  def tensor_map_erase(input_handle, key, value_dtype):
    m = gen_map_ops.tensor_map_erase(input_handle, key, value_dtype)
    return m
  
  
  def tensor_map_has_key(input_handle, key):
    return gen_map_ops.tensor_map_has_key(input_handle, key)
  
  
  @ops.RegisterGradient("TensorMapLookup")
  def LookupGrad(op, dval):
    _, k = op.inputs
    map_grad = empty_tensor_map()
    map_grad = tensor_map_insert(map_grad, k, dval)
    key_grad = None
    return map_grad, key_grad
  
  
  @ops.RegisterGradient("TensorMapInsert")
  def InsertGrad(op, dmap):
    _, k, v = op.inputs
    key_grad = None
    (value_grad, map_grad) = control_flow_ops.cond(tensor_map_has_key(dmap, k),
                                                   lambda: (tensor_map_lookup(dmap, k, v.dtype), tensor_map_erase(dmap, k, v.dtype)),
                                                   lambda: (array_ops.zeros_like(v), dmap))
    return map_grad, key_grad, value_grad
  
  @ops.RegisterGradient("TensorMapErase")
  def EraseGrad(op, dmap):
    _, k = op.inputs
    key_grad = None
    map_grad = dmap
    return map_grad, key_grad
  
subject: clean up