class_docstr: An operation that represents creating a SavedModel with bound values.
  
  This operation represents creating a SavedModel.  Its output is a
  PCollection containing a single element which is the directory containing the
  `SavedModel`.  The inputs are a PCollection of tensor bindings.  A tensor
  binding is the specification of a tensor and a value that it should be
  replaced with in the graph.
  
  This allows us to create a `SavedModel` in a deferred manner, which depends on
  deferred values (the tensor bindings) which were not known when the Beam graph
  was constructed.
  
  
  Attributes:
    table_initializers: A list of table initializer ops that should be run as
        part of this SavedModel.
    output_signature: The output signature of this `SavedModel`, as a dictionary
        whose keys are feature names and values are `Tensor`s or
        `SparseTensor`s.
    label: A unique label for this operation.
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code: def _get_tensor_type_name(self, tensor):
      if isinstance(tensor, tf.Tensor):
          return "Tensor"
      elif isinstance(tensor, tf.SparseTensor):
          return "SparseTensor"
      elif isinstance(tensor, tf.RaggedTensor):
          return "RaggedTensor"
      raise ValueError(
          "Got a {}, expected a Tensor or SparseTensor".format(
              type(tensor)
          )
      )
  
imports: import tensorflow
  from tensorflow_transform import nodes
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 1 for calling the CreateSavedModel obj's underlying member method code with example usage: obj._get_tensor_type_name(tensor) and returns: str, str, str