def polar(abs_, angle):
    """Constructs a complex tensor whose elements are Cartesian
    coordinates corresponding to the polar coordinates
    with absolute value `abs` and angle `angle`.

    The operation is numerically equivalent to `torch.polar()`.
    It is not equivalent to `scipy.lingalg.polar()` which performs
    Singular Value Decomposition.

    Given the magnitude (`abs_`) and angle (`angle`), this function computes the
    corresponding complex number in the form of `real + imaginary * 1j`, where:
    - `real = abs_ * cos(angle)`
    - `imaginary = abs_ * sin(angle)`

    Args:
        abs_: The magnitude (absolute value) of the complex number.
        angle: The angle (in radians) of the complex number.

    Returns:
        A complex number (or array of complex numbers) with the same shape as
        `abs_` and `angle`.

    Example:

    >>> abs_ = keras.random.normal((1, 2))
    >>> angle = keras.random.normal((1, 2))
    >>> keras.ops.nn.polar(abs_, angle).shape
    (1, 2)
    >>> keras.ops.nn.polar(abs_, angle)
    Array([[0.63185346-0.59370506j, 0.48960376-0.31677645j]], dtype=complex64)
    """
    if any_symbolic_tensors((abs_, angle)):
        return Polar().symbolic_call(abs_, angle)
    return _polar(abs_, angle)
Constructs a complex tensor whose elements are Cartesian
    coordinates corresponding to the polar coordinates
    with absolute value `abs` and angle `angle`.

    The operation is numerically equivalent to `torch.polar()`.
    It is not equivalent to `scipy.lingalg.polar()` which performs
    Singular Value Decomposition.

    Given the magnitude (`abs_`) and angle (`angle`), this function computes the
    corresponding complex number in the form of `real + imaginary * 1j`, where:
    - `real = abs_ * cos(angle)`
    - `imaginary = abs_ * sin(angle)`

    Args:
        abs_: The magnitude (absolute value) of the complex number.
        angle: The angle (in radians) of the complex number.

    Returns:
        A complex number (or array of complex numbers) with the same shape as
        `abs_` and `angle`.

    Example:

    >>> abs_ = keras.random.normal((1, 2))
    >>> angle = keras.random.normal((1, 2))
    >>> keras.ops.nn.polar(abs_, angle).shape
    (1, 2)
    >>> keras.ops.nn.polar(abs_, angle)
    Array([[0.63185346-0.59370506j, 0.48960376-0.31677645j]], dtype=complex64)
    
keras/src/ops/nn.py