def scale_to_0_1(
    x: common_types.ConsistentTensorType,
    elementwise: bool = False,
    name: Optional[str] = None) -> common_types.ConsistentTensorType:
  """Returns a column which is the input column scaled to have range [0,1].

  Args:
    x: A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
    elementwise: If true, scale each element of the tensor independently.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor`, `SparseTensor`, or `RaggedTensor` containing the input column
    scaled to
    [0, 1]. If the analysis dataset is empty or contains a single distinct
    value, then `x` is scaled using a sigmoid function.
  """
  with tf.compat.v1.name_scope(name, 'scale_to_0_1'):
    return _scale_by_min_max_internal(
        x,
        key=None,
        output_min=0,
        output_max=1,
        elementwise=elementwise,
        key_vocabulary_filename=None)
Returns a column which is the input column scaled to have range [0,1].

  Args:
    x: A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
    elementwise: If true, scale each element of the tensor independently.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor`, `SparseTensor`, or `RaggedTensor` containing the input column
    scaled to
    [0, 1]. If the analysis dataset is empty or contains a single distinct
    value, then `x` is scaled using a sigmoid function.
  
tensorflow_transform/mappers.py