class_docstr: Reload a Keras model/layer that was saved via SavedModel / ExportArchive.
  
  Arguments:
      filepath: `str` or `pathlib.Path` object. The path to the SavedModel.
      call_endpoint: Name of the endpoint to use as the `call()` method
          of the reloaded layer. If the SavedModel was created
          via `model.export()`,
          then the default endpoint name is `'serve'`. In other cases
          it may be named `'serving_default'`.
  
  Example:
  
  ```python
  model.export("path/to/artifact")
  reloaded_layer = TFSMLayer("path/to/artifact")
  outputs = reloaded_layer(inputs)
  ```
  
  The reloaded object can be used like a regular Keras layer, and supports
  training/fine-tuning of its trainable weights. Note that the reloaded
  object retains none of the internal structure or custom methods of the
  original object -- it's a brand new layer created around the saved
  function.
  
  **Limitations:**
  
  * Only call endpoints with a single `inputs` tensor argument
  (which may optionally be a dict/tuple/list of tensors) are supported.
  For endpoints with multiple separate input tensor arguments, consider
  subclassing `TFSMLayer` and implementing a `call()` method with a
  custom signature.
  * If you need training-time behavior to differ from inference-time behavior
  (i.e. if you need the reloaded object to support a `training=True` argument
  in `__call__()`), make sure that the training-time call function is
  saved as a standalone endpoint in the artifact, and provide its name
  to the `TFSMLayer` via the `call_training_endpoint` argument.
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code: def call(self, inputs, training=False, **kwargs):
      if training:
          if self.call_training_endpoint:
              return self.call_training_endpoint_fn(inputs, **kwargs)
      return self.call_endpoint_fn(inputs, **kwargs)
  
imports: from absl import logging
  from keras import backend
  from keras.api_export import keras_export
  from keras.layers import Layer
  from keras.models import Functional
  from keras.models import Sequential
  from keras.utils import io_utils
  from keras.utils.module_utils import tensorflow
label_desc: Use image node_id 3 for calling the TFSMLayer obj's underlying member method code with example usage: obj.call(inputs, training) and returns: self, self