def scale_by_min_max(
    x: common_types.ConsistentTensorType,
    output_min: float = 0.0,
    output_max: float = 1.0,
    elementwise: bool = False,
    name: Optional[str] = None) -> common_types.ConsistentTensorType:
  """Scale a numerical column into the range [output_min, output_max].

  Args:
    x: A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
    output_min: The minimum of the range of output values.
    output_max: The maximum of the range of output values.
    elementwise: If true, scale each element of the tensor independently.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor` containing the input column scaled to [output_min, output_max].
    If the analysis dataset is empty or contains a singe distinct value, then
    `x` is scaled using a sigmoid function.

  Raises:
    ValueError: If output_min, output_max have the wrong order.
  """
  with tf.compat.v1.name_scope(name, 'scale_by_min_max'):
    return _scale_by_min_max_internal(
        x,
        key=None,
        output_min=output_min,
        output_max=output_max,
        elementwise=elementwise,
        key_vocabulary_filename=None)
Scale a numerical column into the range [output_min, output_max].

  Args:
    x: A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
    output_min: The minimum of the range of output values.
    output_max: The maximum of the range of output values.
    elementwise: If true, scale each element of the tensor independently.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor` containing the input column scaled to [output_min, output_max].
    If the analysis dataset is empty or contains a singe distinct value, then
    `x` is scaled using a sigmoid function.

  Raises:
    ValueError: If output_min, output_max have the wrong order.
  
tensorflow_transform/mappers.py