def slice(inputs, start_indices, shape):
    """Return a slice of an input tensor.

    At a high level, this operation is an explicit replacement for array slicing
    e.g. `inputs[start_indices: start_indices + shape]`.
    Unlike slicing via brackets, this operation will accept tensor start
    indices on all backends, which is useful when indices dynamically computed
    via other tensor operations.

    ```python
    inputs = np.zeros((5, 5))
    start_indices = np.array([3, 3])
    shape = np.array([2, 2])
    inputs = keras.ops.slice(inputs, start_indices, shape)
    ```

    Args:
        inputs: A tensor, the tensor to be updated.
        start_indices: A list/tuple of shape `(inputs.ndim,)`, specifying
            the starting indices for updating.
        shape: The full shape of the returned slice.

    Returns:
        A tensor, has the same shape and dtype as `inputs`.
    """
    if any_symbolic_tensors((inputs, start_indices, shape)):
        return Slice().symbolic_call(inputs, start_indices, shape)
    return backend.core.slice(inputs, start_indices, shape)
Return a slice of an input tensor.

    At a high level, this operation is an explicit replacement for array slicing
    e.g. `inputs[start_indices: start_indices + shape]`.
    Unlike slicing via brackets, this operation will accept tensor start
    indices on all backends, which is useful when indices dynamically computed
    via other tensor operations.

    ```python
    inputs = np.zeros((5, 5))
    start_indices = np.array([3, 3])
    shape = np.array([2, 2])
    inputs = keras.ops.slice(inputs, start_indices, shape)
    ```

    Args:
        inputs: A tensor, the tensor to be updated.
        start_indices: A list/tuple of shape `(inputs.ndim,)`, specifying
            the starting indices for updating.
        shape: The full shape of the returned slice.

    Returns:
        A tensor, has the same shape and dtype as `inputs`.
    
keras/src/ops/core.py