code: def scale_to_z_score(
      x: common_types.ConsistentTensorType,
      elementwise: bool = False,
      name: Optional[str] = None,
      output_dtype: Optional[tf.DType] = None,
  ) -> common_types.ConsistentTensorType:
      """Returns a standardized column with mean 0 and variance 1.
  
      Scaling to z-score subtracts out the mean and divides by standard deviation.
      Note that the standard deviation computed here is based on the biased variance
      (0 delta degrees of freedom), as computed by analyzers.var.
  
      Args:
        x: A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
        elementwise: If true, scales each element of the tensor independently;
          otherwise uses the mean and variance of the whole tensor.
        name: (Optional) A name for this operation.
        output_dtype: (Optional) If not None, casts the output tensor to this type.
  
      Returns:
        A `Tensor`, `SparseTensor`, or `RaggedTensor` containing the input column
        scaled to mean 0
        and variance 1 (standard deviation 1), given by: (x - mean(x)) / std_dev(x).
        If `x` is floating point, the mean will have the same type as `x`. If `x` is
        integral, the output is cast to tf.float32. If the analysis dataset is empty
        or contains a single distinct value, then the input is returned without
        scaling.
  
        Note that TFLearn generally permits only tf.int64 and tf.float32, so casting
        this scaler's output may be necessary.
      """
      with tf.compat.v1.name_scope(name, "scale_to_z_score"):
          return _scale_to_z_score_internal(
              x=x,
              key=None,
              elementwise=elementwise,
              key_vocabulary_filename=None,
              output_dtype=output_dtype,
          )
  
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 10 for calling a global function with example usage: scale_to_z_score(x, elementwise, name, output_dtype) and returns: _scale_to_z_score_internal