Fix #8350: Clarify LocalNormalizedCrossCorrelationLoss docstring (#8639)

## Description
This PR improves the docstring for `LocalNormalizedCrossCorrelationLoss`
to address the ambiguities identified in
  #8350.

  ## Problem
  The current docstring does not clearly document:
  - The range of returned loss values
  - Whether the loss should be minimized or maximized
  - How to interpret high vs. low loss values

  ## Solution
Enhanced the class docstring with comprehensive documentation including
Returns section (value range, optimization
direction) and Note section (implementation details, interpretation
guidelines).

  ## Changes
- Added `Returns` section with explicit value range and optimization
direction
  - Added `Note` section explaining transformations and interpretation
  - Reorganized Args to class level for better discoverability
  - Followed MONAI formatting conventions

  ## Testing
  - [x] Verified docstring syntax is correct
  - [x] Confirmed technical accuracy by analyzing implementation
  - [x] Validated all three issue requirements are addressed

Signed-off-by: Mohamed Salah <eng.mohamed.tawab@gmail.com>

Author: engmohamedsalah

monai/losses/image_dissimilarity.py

@@ -51,6 +51,7 @@ def make_gaussian_kernel(kernel_size: int) -> torch.Tensor:
 class LocalNormalizedCrossCorrelationLoss(_Loss):
     """
     Local squared zero-normalized cross-correlation.
+
     The loss is based on a moving kernel/window over the y_true/y_pred,
     within the window the square of zncc is calculated.
     The kernel can be a rectangular / triangular / gaussian window.
@@ -59,6 +60,35 @@ class LocalNormalizedCrossCorrelationLoss(_Loss):
     Adapted from:
         https://github.com/voxelmorph/voxelmorph/blob/legacy/src/losses.py
         DeepReg (https://github.com/DeepRegNet/DeepReg)
+
+    Args:
+        spatial_dims: number of spatial dimensions, {``1``, ``2``, ``3``}. Defaults to 3.
+        kernel_size: kernel spatial size, must be odd.
+        kernel_type: {``"rectangular"``, ``"triangular"``, ``"gaussian"``}. Defaults to ``"rectangular"``.
+        reduction: {``"none"``, ``"mean"``, ``"sum"``}
+            Specifies the reduction to apply to the output. Defaults to ``"mean"``.
+
+            - ``"none"``: no reduction will be applied.
+            - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
+            - ``"sum"``: the output will be summed.
+        smooth_nr: a small constant added to the numerator to avoid nan.
+        smooth_dr: a small constant added to the denominator to avoid nan.
+
+    Returns:
+        torch.Tensor: The computed loss value. The output range is approximately [-1, 0], where:
+            - Values closer to -1 indicate higher correlation (better match)
+            - Values closer to 0 indicate lower correlation (worse match)
+            - This loss should be **minimized** during optimization
+
+    Note:
+        The implementation computes the squared normalized cross-correlation coefficient
+        and then negates it, transforming the correlation maximization problem into a
+        loss minimization problem suitable for standard PyTorch optimizers.
+
+        Interpretation:
+            - Loss ≈ -1: Perfect correlation between images
+            - Loss ≈ 0: No correlation between images
+            - Lower (more negative) values indicate better alignment
     """
 
     def __init__(
@@ -70,21 +100,6 @@ def __init__(
         smooth_nr: float = 0.0,
         smooth_dr: float = 1e-5,
     ) -> None:
-        """
-        Args:
-            spatial_dims: number of spatial dimensions, {``1``, ``2``, ``3``}. Defaults to 3.
-            kernel_size: kernel spatial size, must be odd.
-            kernel_type: {``"rectangular"``, ``"triangular"``, ``"gaussian"``}. Defaults to ``"rectangular"``.
-            reduction: {``"none"``, ``"mean"``, ``"sum"``}
-                Specifies the reduction to apply to the output. Defaults to ``"mean"``.
-
-                - ``"none"``: no reduction will be applied.
-                - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
-                - ``"sum"``: the output will be summed.
-            smooth_nr: a small constant added to the numerator to avoid nan.
-            smooth_dr: a small constant added to the denominator to avoid nan.
-
-        """
         super().__init__(reduction=LossReduction(reduction).value)
 
         self.ndim = spatial_dims