code: def value_and_gradient(
      f,
      xs,
      output_gradients=None,
      use_gradient_tape=False,
      unconnected_gradients=None,
      name=None,
  ):
      """Computes `f(*xs)` and its gradients wrt to `*xs`.
  
      Args:
        f: Python `callable` to be differentiated. If `f` returns a scalar, this
          scalar will be differentiated. If `f` returns a tensor or list of tensors,
          by default a scalar will be computed by adding all their values to produce
          a single scalar. If desired, the tensors can be elementwise multiplied by
          the tensors passed as the `dy` keyword argument to the returned gradient
          function.
        xs: Python list of parameters of `f` for which to differentiate. (Can also
          be single `Tensor`.)
        output_gradients: A `Tensor` or list of `Tensor`s the same size as the
          result `ys = f(*xs)` and holding the gradients computed for each `y` in
          `ys`. This argument is forwarded to the underlying gradient implementation
          (i.e., either the `grad_ys` argument of `tf.gradients` or the
          `output_gradients` argument of `tf.GradientTape.gradient`).
        use_gradient_tape: Python `bool` indicating that `tf.GradientTape` should be
          used regardless of `tf.executing_eagerly()` status.
          Default value: `False`.
        unconnected_gradients: An enum `tf.UnconnectedGradients` which specifies the
          gradient value returned when the given input tensors are unconnected.
          Default value: `None`, which maps to `tf.UnconnectedGradients.NONE`.
        name: Python `str` name prefixed to ops created by this function.
          Default value: `None` (i.e., `'value_and_gradient'`).
  
      Returns:
        A tuple of two elements. The first one is a `Tensor` representing the value
        of the function at `xs` and the second one is either a `Tensor` or a list of
        `Tensor`s representing the gradient of `f(*xs)` wrt `xs`.
        y: `y = f(*xs)`.
        dydx: Gradient of `y` wrt each of `xs`.
      """
      unconnected_gradients = (
          unconnected_gradients or tf.UnconnectedGradients.NONE
      )
      xs, is_xs_list_like = _prepare_args(xs)
      with tf.name_scope(name or "value_and_gradient"):
          if tf.executing_eagerly() or use_gradient_tape:
              with tf.GradientTape() as tape:
                  for x in xs:
                      tape.watch(x)
                  y = f(*xs)
              grad = tape.gradient(
                  y,
                  xs,
                  output_gradients=output_gradients,
                  unconnected_gradients=unconnected_gradients,
              )
          else:
              y = f(*xs)
              grad = tf.gradients(
                  ys=y,
                  xs=xs,
                  grad_ys=output_gradients,
                  unconnected_gradients=unconnected_gradients,
              )
          if is_xs_list_like:
              return y, grad
          else:
              return y, grad[0]
  
file_path: tf-quant-finance/tf_quant_finance/math/gradient.py
imports: import functools
  import tensorflow.compat.v2
label_desc: Use image node_id 3 for calling a global function with example usage: value_and_gradient(f, xs, output_gradients, use_gradient_tape, unconnected_gradients, name) and returns: y,  grad, y,  grad