create separate test configs for multimer and monomer

tests/config.py

 import ml_collections as mlc
 
-consts = mlc.ConfigDict(
+
+monomer_consts = mlc.ConfigDict(
+    {
+        "model": "model_1_ptm",  # monomer:model_1_ptm, multimer: model_1_multimer_v3
+        "is_multimer": False,  # monomer: False, multimer: True
+        "chunk_size": 4,
+        "batch_size": 2,
+        "n_res": 22,
+        "n_seq": 13,
+        "n_templ": 3,
+        "n_extra": 17,
+        "n_heads_extra_msa": 8,
+        "eps": 5e-4,
+        # For compatibility with DeepMind's pretrained weights, it's easiest for
+        # everyone if these take their real values.
+        "c_m": 256,
+        "c_z": 128,
+        "c_s": 384,
+        "c_t": 64,
+        "c_e": 64,
+        "msa_logits": 23,  # monomer: 23, multimer: 22
+        "template_mmcif_dir": None  # Set for test_multimer_datamodule
+    }
+)
+
+multimer_consts = mlc.ConfigDict(
     {
         "model": "model_1_multimer_v3",  # monomer:model_1_ptm, multimer: model_1_multimer_v3
         "is_multimer": True,  # monomer: False, multimer: True
@@ -24,6 +49,8 @@ consts = mlc.ConfigDict(
     }
 )
 
+consts = monomer_consts 
+
 config = mlc.ConfigDict(
     {
         "data": {