[ENH] Added test parameters for the LSTM FCNN network (#6281)

I have added 2 test parameters for the LSTM-FCNN network returned via
the `get_test_params` function. The test parameters aim to explore 2
quite different variations of the model in the hope to capture a broad
spectrum of the model's parameters.

.all-contributorsrc

@@ -2756,6 +2756,15 @@
         "doc"
       ]
     },
+    {
+      "login": "ssabarwal",
+      "name": "Shlok Sabarwal",
+      "avatar_url": "https://gravatar.com/avatar/cbdbaac712ae282d730cd3028e862d45?s=400&d=robohash&r=x",
+      "prifle": "https://www.github.com/shlok191/",
+      "contributions": [
+        "code"
+      ]
+    },
     {
       "login": "mobley-trent",
       "name": "Eddy Oyieko",

sktime/networks/lstmfcn.py

@@ -123,3 +123,43 @@ def build_network(self, input_shape, **kwargs):
         output_layer = keras.layers.concatenate([x, y])
 
         return input_layer, output_layer
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return `"default"` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            `create_test_instance` uses the first (or only) dictionary in `params`
+        """
+        params = [
+            # Advanced model version
+            {
+                "kernel_sizes": (8, 5, 3),  # Keep standard kernel sizes
+                "filter_sizes": (128, 256, 128),  # Keep standard kernel counts
+                "lstm_size": 8,
+                "dropout": 0.25,  # Maintain lower dropout rate for attention model
+                "attention": True,
+            },
+            # Simpler model version
+            {
+                "kernel_sizes": (4, 2, 1),  # Reduce kernel sizes
+                "filter_sizes": (32, 64, 32),  # Reduc filter sizes for cheaper model
+                "lstm_size": 8,  # Keeping LSTM output size fixed
+                "dropout": 0.75,  # Maintain higher dropout rate for non attention model
+                "attention": False,
+            },
+            {},
+        ]
+
+        return params