class_docstr: Represents the output of a regression head.
class_docstr_tok: ["Represents","the","output","of","a","regression","head","."]
docstr: Constructor for `RegressionOutput`.
  
  Args:
    value: a float `Tensor` giving the predicted values.  Required.
  
  Raises:
    ValueError: if the value is not a `Tensor` with dtype tf.float32.
docstr_tok: ["Constructor","for","`","RegressionOutput","`",".","Args",":","value",":","a","float","`","Tensor","`","giving","the","predicted","values",".","Required",".","Raises",":","ValueError",":","if","the","value","is","not","a","`","Tensor","`","with","dtype","tf.float32","."]
code: def __init__(self, value):
      """Constructor for `RegressionOutput`.
  
      Args:
        value: a float `Tensor` giving the predicted values.  Required.
  
      Raises:
        ValueError: if the value is not a `Tensor` with dtype tf.float32.
      """
      if not (
          isinstance(value, tensor.Tensor) and value.dtype.is_floating
      ):
          raise ValueError(
              "Regression output value must be a float32 Tensor; "
              "got {}".format(value)
          )
      self._value = value
  
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 1 to create a new RegressionOutput object from inherited base classes: ExportOutput with example: obj = RegressionOutput(value)