class_docstr: Represents an output of a model that can be served.
  
  These typically correspond to model heads.
class_docstr_tok: ["Represents","an","output","of","a","model","that","can","be","served",".","These","typically","correspond","to","model","heads","."]
docstr: Wraps raw tensors as dicts and checks type.
  
  Note that we create a new dict here so that we can overwrite the keys
  if necessary.
  
  Args:
    outputs: A `Tensor` or a dict of string to `Tensor`.
    single_output_default_name: A string key for use in the output dict
      if the provided `outputs` is a raw tensor.
    error_label: descriptive string for use in error messages. If none,
      single_output_default_name will be used.
  
  Returns:
    A dict of tensors
  
  Raises:
    ValueError: if the outputs dict keys are not strings or tuples of strings
      or the values are not Tensors.
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code: def _wrap_and_check_outputs(
      self, outputs, single_output_default_name, error_label=None
  ):
      """Wraps raw tensors as dicts and checks type.
  
      Note that we create a new dict here so that we can overwrite the keys
      if necessary.
  
      Args:
        outputs: A `Tensor` or a dict of string to `Tensor`.
        single_output_default_name: A string key for use in the output dict
          if the provided `outputs` is a raw tensor.
        error_label: descriptive string for use in error messages. If none,
          single_output_default_name will be used.
  
      Returns:
        A dict of tensors
  
      Raises:
        ValueError: if the outputs dict keys are not strings or tuples of strings
          or the values are not Tensors.
      """
      if not isinstance(outputs, dict):
          outputs = {single_output_default_name: outputs}
  
      output_dict = {}
      for key, value in outputs.items():
          error_name = error_label or single_output_default_name
          key = self._check_output_key(key, error_name)
          if not isinstance(value, tensor.Tensor):
              raise ValueError(
                  "{} output value must be a Tensor; got {}.".format(
                      error_name, value
                  )
              )
  
          output_dict[key] = value
      return output_dict
  
imports: import abc
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras.saving.utils_v1 import signature_def_utils
  from tensorflow.python.saved_model import signature_def_utils
label_desc: Use image node_id 3 for calling the ExportOutput obj's underlying member method code with example usage: obj._wrap_and_check_outputs(outputs, single_output_default_name, error_label) and returns: output_dict