code: 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,
          )
  
imports: import os
  from typing import Any, Callable, Iterable, Optional, Tuple, Union
  import tensorflow
  from tensorflow_transform import analyzers
  from tensorflow_transform import common
  from tensorflow_transform import common_types
  from tensorflow_transform import gaussianization
  from tensorflow_transform import schema_inference
  from tensorflow_transform import tf_utils
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 8 for calling a global function with example usage: scale_to_0_1(x, elementwise, name) and returns: _scale_by_min_max_internal