class_docstr: TF2-compatible VariableStore that avoids collections & tracks regularizers.
  
  New variable names and new variables can be created; all stored
  variables are initialized with the initializer passed to __init__.
  
  All variables get created in `tf.init_scope.` to avoid a bad
  interaction between `tf.function` `FuncGraph` internals, Keras
  Functional Models, and TPUStrategy variable initialization.
  
  Attributes:
    vars: a dictionary with string names (same as passed in GetVar) as keys and
      the corresponding TensorFlow Variables as values.
docstr: Create a variable store.
code: def __init__(self):
      """Create a variable store."""
      self._vars = (
          {}
      )  # A dictionary of the stored TensorFlow variables.
      self._regularizers = (
          {}
      )  # A dict mapping var names to their regularizers.
      self._store_eager_variables = True
  
imports: import functools
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras.engine import base_layer
  from tensorflow.python.keras.utils import tf_contextlib
  from tensorflow.python.keras.utils import tf_inspect
  from tensorflow.python.module import module
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 1 to create a new _EagerVariableStore object from inherited base classes: object with example: obj = _EagerVariableStore()