[Tests] Improve transformers model test suite coverage - Temporal Transformer (#8932)

* add test for temporal transformer

* remove unused variable

* fix code quality

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

tests/models/transformers/test_models_transformer_temporal.py

+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import torch
+
+from diffusers.models.transformers import TransformerTemporalModel
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    torch_device,
+)
+
+from ..test_modeling_common import ModelTesterMixin
+
+
+enable_full_determinism()
+
+
+class TemporalTransformerTests(ModelTesterMixin, unittest.TestCase):
+    model_class = TransformerTemporalModel
+    main_input_name = "hidden_states"
+
+    @property
+    def dummy_input(self):
+        batch_size = 2
+        num_channels = 4
+        height = width = 32
+
+        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+
+        return {
+            "hidden_states": hidden_states,
+            "timestep": timestep,
+        }
+
+    @property
+    def input_shape(self):
+        return (4, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (4, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "num_attention_heads": 8,
+            "attention_head_dim": 4,
+            "in_channels": 4,
+            "num_layers": 1,
+            "norm_num_groups": 1,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict