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: Provide a default initializer and a corresponding value.
  
  Args:
    name: see get_variable.
    shape: see get_variable.
    dtype: see get_variable.
  
  Returns:
    initializer and initializing_from_value. See get_variable above.
  
  Raises:
    ValueError: When giving unsupported dtype.
code: def _get_default_initializer(
      self, name, shape=None, dtype=dtypes.float32
  ):
      """Provide a default initializer and a corresponding value.
  
      Args:
        name: see get_variable.
        shape: see get_variable.
        dtype: see get_variable.
  
      Returns:
        initializer and initializing_from_value. See get_variable above.
  
      Raises:
        ValueError: When giving unsupported dtype.
      """
      del shape
      # If dtype is DT_FLOAT, provide a uniform unit scaling initializer
      if dtype.is_floating:
          initializer = init_ops.glorot_uniform_initializer()
          initializing_from_value = False
      # If dtype is DT_INT/DT_UINT, provide a default value `zero`
      # If dtype is DT_BOOL, provide a default value `FALSE`
      elif (
          dtype.is_integer
          or dtype.is_unsigned
          or dtype.is_bool
          or dtype == dtypes.string
      ):
          initializer = init_ops.zeros_initializer()
          initializing_from_value = False
      # NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
      else:
          raise ValueError(
              "An initializer for variable %s of %s is required"
              % (name, dtype.base_dtype)
          )
  
      return initializer, initializing_from_value
  
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 5 for calling the _EagerVariableStore obj's underlying member method code with example usage: obj._get_default_initializer(name, shape, dtype) and returns: initializer,  initializing_from_value