def mean_squared_logarithmic_error(y_true, y_pred):
    """Computes the mean squared logarithmic error between `y_true` & `y_pred`.

    Formula:

    ```python
    loss = mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)
    ```

    Note that `y_pred` and `y_true` cannot be less or equal to 0. Negative
    values and 0 values will be replaced with `keras.backend.epsilon()`
    (default to `1e-7`).

    Args:
        y_true: Ground truth values with shape = `[batch_size, d0, .. dN]`.
        y_pred: The predicted values with shape = `[batch_size, d0, .. dN]`.

    Returns:
        Mean squared logarithmic error values with shape = `[batch_size, d0, ..
        dN-1]`.

    Example:

    >>> y_true = np.random.randint(0, 2, size=(2, 3))
    >>> y_pred = np.random.random(size=(2, 3))
    >>> loss = keras.losses.mean_squared_logarithmic_error(y_true, y_pred)
    """
    epsilon = ops.convert_to_tensor(backend.epsilon())
    y_pred = ops.convert_to_tensor(y_pred)
    y_true = ops.convert_to_tensor(y_true, dtype=y_pred.dtype)
    y_true, y_pred = squeeze_or_expand_to_same_rank(y_true, y_pred)
    first_log = ops.log(ops.maximum(y_pred, epsilon) + 1.0)
    second_log = ops.log(ops.maximum(y_true, epsilon) + 1.0)
    return ops.mean(ops.square(first_log - second_log), axis=-1)
Computes the mean squared logarithmic error between `y_true` & `y_pred`.

    Formula:

    ```python
    loss = mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)
    ```

    Note that `y_pred` and `y_true` cannot be less or equal to 0. Negative
    values and 0 values will be replaced with `keras.backend.epsilon()`
    (default to `1e-7`).

    Args:
        y_true: Ground truth values with shape = `[batch_size, d0, .. dN]`.
        y_pred: The predicted values with shape = `[batch_size, d0, .. dN]`.

    Returns:
        Mean squared logarithmic error values with shape = `[batch_size, d0, ..
        dN-1]`.

    Example:

    >>> y_true = np.random.randint(0, 2, size=(2, 3))
    >>> y_pred = np.random.random(size=(2, 3))
    >>> loss = keras.losses.mean_squared_logarithmic_error(y_true, y_pred)
    
keras/src/losses/losses.py