parameters: (ckpt_dir_or_file, assignment_map)
docstring: Replaces `tf.Variable` initializers so they load from a checkpoint file.
  
    @compatibility(TF2)
    `tf.compat.v1.train.init_from_checkpoint` is not recommended for restoring
    variable values in TF2.
  
    To restore checkpoints in TF2, please use
    `tf.keras.Model.load_weights` or `tf.train.Checkpoint.restore`. These APIs use
    use an [object-based method of checkpointing]
    (https://www.tensorflow.org/guide/checkpoint#loading_mechanics), while
    `tf.compat.v1.init_from_checkpoint` relies on a more-fragile variable-name
    based method of checkpointing. There is no object-based equivalent of
    `init_from_checkpoint` in TF2.
  
    Please re-write your checkpoints immediately using the object-based APIs,
    see [migration guide]
    (https://www.tensorflow.org/guide/migrate#checkpoint_compatibility) for more
    details.
  
    You can load a name-based checkpoint written by `tf.compat.v1.train.Saver`
    using `tf.train.Checkpoint.restore` or `tf.keras.Model.load_weights`. However,
    you may have to change the names of the variables in your model to match the
    variable names in the name-based checkpoint, which can be viewed with
    `tf.train.list_variables(path)`.
  
    Another option is to create an `assignment_map` that maps the name of the
    variables in the name-based checkpoint to the variables in your model, eg:
    ```
    {
        'sequential/dense/bias': model.variables[0],
        'sequential/dense/kernel': model.variables[1]
    }
    ```
    and use `tf.compat.v1.train.init_from_checkpoint(path, assignment_map)` to
    restore the name-based checkpoint.
  
    After restoring, re-encode your checkpoint using `tf.train.Checkpoint.save`
    or `tf.keras.Model.save_weights`.
  
    @end_compatibility
  
    Values are not loaded immediately, but when the initializer is run
    (typically by running a `tf.compat.v1.global_variables_initializer` op).
  
    Note: This overrides default initialization ops of specified variables and
    redefines dtype.
  
    Assignment map supports following syntax:
  
    * `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in
      current `scope_name` from `checkpoint_scope_name` with matching tensor
      names.
    * `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -
      will initialize `scope_name/variable_name` variable
      from `checkpoint_scope_name/some_other_variable`.
    * `'scope_variable_name': variable` - will initialize given `tf.Variable`
      object with tensor 'scope_variable_name' from the checkpoint.
    * `'scope_variable_name': list(variable)` - will initialize list of
      partitioned variables with tensor 'scope_variable_name' from the checkpoint.
    * `'/': 'scope_name/'` - will load all variables in current `scope_name` from
      checkpoint's root (e.g. no scope).
  
    Supports loading into partitioned variables, which are represented as
    `'<variable>/part_<part #>'`.
  
    Assignment map can be a dict, or a list of pairs.  The latter is
    necessary to initialize multiple variables in the current graph from
    the same variable in the checkpoint.
  
    Example:
  
    ```python
  
    # Say, '/tmp/model.ckpt' has the following tensors:
    #  -- name='old_scope_1/var1', shape=[20, 2]
    #  -- name='old_scope_1/var2', shape=[50, 4]
    #  -- name='old_scope_2/var3', shape=[100, 100]
  
    # Create new model's variables
    with tf.compat.v1.variable_scope('new_scope_1'):
      var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],
                             initializer=tf.compat.v1.zeros_initializer())
    with tf.compat.v1.variable_scope('new_scope_2'):
      var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],
                             initializer=tf.compat.v1.zeros_initializer())
      # Partition into 5 variables along the first axis.
      var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],
                             initializer=tf.compat.v1.zeros_initializer(),
                             partitioner=lambda shape, dtype: [5, 1])
  
    # Initialize all variables in `new_scope_1` from `old_scope_1`.
    init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1/'})
  
    # Use names to specify which variables to initialize from checkpoint.
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_1/var1': 'new_scope_1/var1',
                          'old_scope_1/var2': 'new_scope_2/var2'})
  
    # Or use tf.Variable objects to identify what to initialize.
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_1/var1': var1,
                          'old_scope_1/var2': var2})
  
    # Initialize partitioned variables using variable's name
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_2/var3': 'new_scope_2/var3'})
  
    # Or specify the list of tf.Variable objects.
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_2/var3': var3._get_variable_list()})
  
    ```
  
    Args:
      ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
      assignment_map: Dict, or a list of key-value pairs, where keys are names
        of the variables in the checkpoint and values are current variables or
        names of current variables (in default graph).
  
    Raises:
      ValueError: If missing variables in current graph, or if missing
        checkpoints or tensors in checkpoints.
docstring_summary: Replaces `tf.Variable` initializers so they load from a checkpoint file.
docstring_tokens: Replaces tf . Variable initializers so they load from a checkpoint file .
function: def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
    """Replaces `tf.Variable` initializers so they load from a checkpoint file.
  
    @compatibility(TF2)
    `tf.compat.v1.train.init_from_checkpoint` is not recommended for restoring
    variable values in TF2.
  
    To restore checkpoints in TF2, please use
    `tf.keras.Model.load_weights` or `tf.train.Checkpoint.restore`. These APIs use
    use an [object-based method of checkpointing]
    (https://www.tensorflow.org/guide/checkpoint#loading_mechanics), while
    `tf.compat.v1.init_from_checkpoint` relies on a more-fragile variable-name
    based method of checkpointing. There is no object-based equivalent of
    `init_from_checkpoint` in TF2.
  
    Please re-write your checkpoints immediately using the object-based APIs,
    see [migration guide]
    (https://www.tensorflow.org/guide/migrate#checkpoint_compatibility) for more
    details.
  
    You can load a name-based checkpoint written by `tf.compat.v1.train.Saver`
    using `tf.train.Checkpoint.restore` or `tf.keras.Model.load_weights`. However,
    you may have to change the names of the variables in your model to match the
    variable names in the name-based checkpoint, which can be viewed with
    `tf.train.list_variables(path)`.
  
    Another option is to create an `assignment_map` that maps the name of the
    variables in the name-based checkpoint to the variables in your model, eg:
    ```
    {
        'sequential/dense/bias': model.variables[0],
        'sequential/dense/kernel': model.variables[1]
    }
    ```
    and use `tf.compat.v1.train.init_from_checkpoint(path, assignment_map)` to
    restore the name-based checkpoint.
  
    After restoring, re-encode your checkpoint using `tf.train.Checkpoint.save`
    or `tf.keras.Model.save_weights`.
  
    @end_compatibility
  
    Values are not loaded immediately, but when the initializer is run
    (typically by running a `tf.compat.v1.global_variables_initializer` op).
  
    Note: This overrides default initialization ops of specified variables and
    redefines dtype.
  
    Assignment map supports following syntax:
  
    * `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in
      current `scope_name` from `checkpoint_scope_name` with matching tensor
      names.
    * `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -
      will initialize `scope_name/variable_name` variable
      from `checkpoint_scope_name/some_other_variable`.
    * `'scope_variable_name': variable` - will initialize given `tf.Variable`
      object with tensor 'scope_variable_name' from the checkpoint.
    * `'scope_variable_name': list(variable)` - will initialize list of
      partitioned variables with tensor 'scope_variable_name' from the checkpoint.
    * `'/': 'scope_name/'` - will load all variables in current `scope_name` from
      checkpoint's root (e.g. no scope).
  
    Supports loading into partitioned variables, which are represented as
    `'<variable>/part_<part #>'`.
  
    Assignment map can be a dict, or a list of pairs.  The latter is
    necessary to initialize multiple variables in the current graph from
    the same variable in the checkpoint.
  
    Example:
  
    ```python
  
    # Say, '/tmp/model.ckpt' has the following tensors:
    #  -- name='old_scope_1/var1', shape=[20, 2]
    #  -- name='old_scope_1/var2', shape=[50, 4]
    #  -- name='old_scope_2/var3', shape=[100, 100]
  
    # Create new model's variables
    with tf.compat.v1.variable_scope('new_scope_1'):
      var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],
                             initializer=tf.compat.v1.zeros_initializer())
    with tf.compat.v1.variable_scope('new_scope_2'):
      var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],
                             initializer=tf.compat.v1.zeros_initializer())
      # Partition into 5 variables along the first axis.
      var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],
                             initializer=tf.compat.v1.zeros_initializer(),
                             partitioner=lambda shape, dtype: [5, 1])
  
    # Initialize all variables in `new_scope_1` from `old_scope_1`.
    init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1/'})
  
    # Use names to specify which variables to initialize from checkpoint.
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_1/var1': 'new_scope_1/var1',
                          'old_scope_1/var2': 'new_scope_2/var2'})
  
    # Or use tf.Variable objects to identify what to initialize.
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_1/var1': var1,
                          'old_scope_1/var2': var2})
  
    # Initialize partitioned variables using variable's name
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_2/var3': 'new_scope_2/var3'})
  
    # Or specify the list of tf.Variable objects.
    init_from_checkpoint('/tmp/model.ckpt',
                         {'old_scope_2/var3': var3._get_variable_list()})
  
    ```
  
    Args:
      ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
      assignment_map: Dict, or a list of key-value pairs, where keys are names
        of the variables in the checkpoint and values are current variables or
        names of current variables (in default graph).
  
    Raises:
      ValueError: If missing variables in current graph, or if missing
        checkpoints or tensors in checkpoints.
  
    """
    init_from_checkpoint_fn = lambda _: _init_from_checkpoint(
        ckpt_dir_or_file, assignment_map)
    if distribution_strategy_context.get_cross_replica_context():
      init_from_checkpoint_fn(None)
    else:
      distribution_strategy_context.get_replica_context().merge_call(
          init_from_checkpoint_fn)
function_tokens: def init_from_checkpoint ( ckpt_dir_or_file , assignment_map ) : init_from_checkpoint_fn = lambda _ : _init_from_checkpoint ( ckpt_dir_or_file , assignment_map ) if distribution_strategy_context . get_cross_replica_context ( ) : init_from_checkpoint_fn ( None ) else : distribution_strategy_context . get_replica_context ( ) . merge_call ( init_from_checkpoint_fn )