def scale_to_0_1_per_key(
    x: common_types.ConsistentTensorType,
    key: common_types.TensorType,
    elementwise: bool = False,
    key_vocabulary_filename: Optional[str] = None,
    name: Optional[str] = None) -> common_types.ConsistentTensorType:
  # pyformat: disable
  """Returns a column which is the input column scaled to have range [0,1].

  Args:
    x: A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
    key: A `Tensor`, `SparseTensor`, or `RaggedTensor` of type string.
    elementwise: If true, scale each element of the tensor independently.
    key_vocabulary_filename: (Optional) The file name for the per-key file. If
      None, this combiner will assume the keys fit in memory and will not store
      the analyzer result in a file. If '', a file name will be chosen based on
      the current TensorFlow scope. If not '', it should be unique within a
      given preprocessing function.
    name: (Optional) A name for this operation.

  Example:

  >>> def preprocessing_fn(inputs):
  ...   return {
  ...      'scaled': tft.scale_to_0_1_per_key(inputs['x'], inputs['s'])
  ...   }
  >>> raw_data = [dict(x=1, s='a'), dict(x=0, s='b'), dict(x=3, s='a')]
  >>> feature_spec = dict(
  ...     x=tf.io.FixedLenFeature([], tf.float32),
  ...     s=tf.io.FixedLenFeature([], tf.string))
  >>> raw_data_metadata = tft.DatasetMetadata.from_feature_spec(feature_spec)
  >>> with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
  ...   transformed_dataset, transform_fn = (
  ...       (raw_data, raw_data_metadata)
  ...       | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
  >>> transformed_data, transformed_metadata = transformed_dataset
  >>> transformed_data
  [{'scaled': 0.0}, {'scaled': 0.5}, {'scaled': 1.0}]

  Returns:
    A `Tensor`, `SparseTensor`, or `RaggedTensor` containing the input column scaled to [0, 1],
    per key. If the analysis dataset is empty, contains a single distinct value
    or the computed key vocabulary doesn't have an entry for `key`, then `x` is
    scaled using a sigmoid function.
  """
  # pyformat: enable
  with tf.compat.v1.name_scope(name, 'scale_to_0_1_per_key'):
    if key is None:
      raise ValueError('key is None, call `tft.scale_to_0_1` instead')
    return _scale_by_min_max_internal(
        x,
        key=key,
        output_min=0,
        output_max=1,
        elementwise=elementwise,
        key_vocabulary_filename=key_vocabulary_filename)
Returns a column which is the input column scaled to have range [0,1].

  Args:
    x: A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
    key: A `Tensor`, `SparseTensor`, or `RaggedTensor` of type string.
    elementwise: If true, scale each element of the tensor independently.
    key_vocabulary_filename: (Optional) The file name for the per-key file. If
      None, this combiner will assume the keys fit in memory and will not store
      the analyzer result in a file. If '', a file name will be chosen based on
      the current TensorFlow scope. If not '', it should be unique within a
      given preprocessing function.
    name: (Optional) A name for this operation.

  Example:

  >>> def preprocessing_fn(inputs):
  ...   return {
  ...      'scaled': tft.scale_to_0_1_per_key(inputs['x'], inputs['s'])
  ...   }
  >>> raw_data = [dict(x=1, s='a'), dict(x=0, s='b'), dict(x=3, s='a')]
  >>> feature_spec = dict(
  ...     x=tf.io.FixedLenFeature([], tf.float32),
  ...     s=tf.io.FixedLenFeature([], tf.string))
  >>> raw_data_metadata = tft.DatasetMetadata.from_feature_spec(feature_spec)
  >>> with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
  ...   transformed_dataset, transform_fn = (
  ...       (raw_data, raw_data_metadata)
  ...       | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
  >>> transformed_data, transformed_metadata = transformed_dataset
  >>> transformed_data
  [{'scaled': 0.0}, {'scaled': 0.5}, {'scaled': 1.0}]

  Returns:
    A `Tensor`, `SparseTensor`, or `RaggedTensor` containing the input column scaled to [0, 1],
    per key. If the analysis dataset is empty, contains a single distinct value
    or the computed key vocabulary doesn't have an entry for `key`, then `x` is
    scaled using a sigmoid function.
  
tensorflow_transform/mappers.py