Add IdentityN operator for TF Frontend (apache#7452)

* Add frontend code and tests

* Add Frontend Code

Co-authored-by: Ubuntu <ubuntu@ip-172-31-42-251.us-east-2.compute.internal>

python/tvm/relay/frontend/tensorflow.py

@@ -1005,6 +1005,13 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _identityn():
+    def _impl(inputs, attr, params, mod):
+        return inputs
+
+    return _impl
+
+
 def _concatV2():
     def _impl(inputs, attr, params, mod):
         pop_node = inputs.pop(len(inputs) - 1)
@@ -2378,6 +2385,7 @@ def _impl(inputs, attr, params, mod):
     "Greater": _broadcast("greater"),
     "GreaterEqual": _broadcast("greater_equal"),
     "Identity": _identity(),
+    "IdentityN": _identityn(),
     "IsFinite": AttrCvt("isfinite"),
     "IsInf": AttrCvt("isinf"),
     "IsNan": AttrCvt("isnan"),

tests/python/frontend/tensorflow/test_forward.py

@@ -4090,6 +4090,56 @@ def test_forward_dilation():
     _test_dilation2d([1, 3, 3, 1], [2, 2, 1], [1, 1, 1, 1], [1, 1, 2, 1], "VALID")
 
 
+def _test_identityn(data_np_list):
+    with tf.Graph().as_default():
+        data_tensors = []
+        data_tensors_name = []
+        for index, data_np in enumerate(data_np_list):
+            tensor_name = f"data_{index}"
+            data_tensors_name.append(tensor_name + ":0")
+            data_tensors.append(
+                tf.placeholder(shape=data_np.shape, dtype=str(data_np.dtype), name=tensor_name)
+            )
+
+        output = tf.identity_n(data_tensors)
+        output_names = [out.name for out in output]
+        compare_tf_with_tvm(
+            data_np_list,
+            data_tensors_name,
+            output_names,
+        )
+
+
+@pytest.mark.parametrize(
+    "data_np_list",
+    [
+        (
+            [
+                np.array([[1, 1], [0, 3], [0, 1], [2, 0], [3, 1]], dtype=np.int64),
+                np.array([1, 2, 3, 4, 5], dtype=np.int64),
+                np.array([5, 6], dtype=np.int64),
+            ]
+        ),
+        (
+            [
+                np.array([[1, 1], [0, 3], [2, 0], [3, 1]], dtype=np.int64),
+                np.array([1, 2, 3, 4], dtype=np.int64),
+                np.array([5, 6], dtype=np.int64),
+                np.array([True, False, True]),
+            ]
+        ),
+        (
+            [
+                np.array([]),
+                np.array([[]]),
+            ]
+        ),
+    ],
+)
+def test_forward_identityn(data_np_list):
+    _test_identityn(data_np_list)
+
+
 #######################################################################
 # Sparse To Dense
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