fix (torch frontends)(non_linear_activation_functions.py): fixing the…

… implementation of `torch.nn.functional.prelu` to handle broadcasting when the input is a higher dimensional matrix.

ivy/functional/frontends/torch/nn/functional/non_linear_activation_functions.py

@@ -179,6 +179,22 @@ def normalize(input, p=2.0, dim=1, eps=1e-12, out=None):
 
 @to_ivy_arrays_and_back
 def prelu(input, weight):
+    input_dim = input.ndim
+    weight_dim = weight.ndim
+
+    if weight_dim == 0:  
+        pass
+    elif weight_dim == 1:
+        if input_dim >= 2:
+            assert weight.shape[0] == input.shape[1], "Weight size must match input channels"
+
+            # Unsqueeze weight to match input shape
+            weight = weight.expand_dims(axis=0).expand_dims(axis=2).expand_dims(axis=3)
+            # Add more unsqueeze operations if input has more dimensions
+            for i in range(4, input_dim):
+                weight = weight.expand_dims(axis=-1)
+    else:
+        raise ValueError("Weight must be a scalar or 1-D tensor")
     return ivy.add(ivy.maximum(0, input), ivy.multiply(weight, ivy.minimum(0, input)))