def var(x: common_types.TensorType,
        reduce_instance_dims: bool = True,
        name: Optional[str] = None,
        output_dtype: Optional[tf.DType] = None) -> tf.Tensor:
  """Computes the variance of the values of a `Tensor` over the whole dataset.

  Uses the biased variance (0 delta degrees of freedom), as given by
  (x - mean(x))**2 / length(x).

  Args:
    x: `Tensor`, `SparseTensor`, or `RaggedTensor`. Its type must be floating
        point (float{16|32|64}), or integral ([u]int{8|16|32|64}).
    reduce_instance_dims: By default collapses the batch and instance dimensions
        to arrive at a single scalar output. If False, only collapses the batch
        dimension and outputs a vector of the same shape as the input.
    name: (Optional) A name for this operation.
    output_dtype: (Optional) If not None, casts the output tensor to this type.

  Returns:
    A `Tensor` containing the variance. If `x` is floating point, the variance
    will have the same type as `x`. If `x` is integral, the output is cast to
    float32. NaNs and infinite input values are ignored.

  Raises:
    TypeError: If the type of `x` is not supported.
  """
  with tf.compat.v1.name_scope(name, 'var'):
    return _mean_and_var(x, reduce_instance_dims, output_dtype)[1]
Computes the variance of the values of a `Tensor` over the whole dataset.

  Uses the biased variance (0 delta degrees of freedom), as given by
  (x - mean(x))**2 / length(x).

  Args:
    x: `Tensor`, `SparseTensor`, or `RaggedTensor`. Its type must be floating
        point (float{16|32|64}), or integral ([u]int{8|16|32|64}).
    reduce_instance_dims: By default collapses the batch and instance dimensions
        to arrive at a single scalar output. If False, only collapses the batch
        dimension and outputs a vector of the same shape as the input.
    name: (Optional) A name for this operation.
    output_dtype: (Optional) If not None, casts the output tensor to this type.

  Returns:
    A `Tensor` containing the variance. If `x` is floating point, the variance
    will have the same type as `x`. If `x` is integral, the output is cast to
    float32. NaNs and infinite input values are ignored.

  Raises:
    TypeError: If the type of `x` is not supported.
  
tensorflow_transform/analyzers.py