Started the integration tests for retrieval models using fiddle

requirements/dev.txt

@@ -24,3 +24,4 @@ myst-nb==0.13.2
 linkify-it-py==1.0.3
 sphinx-multiversion@git+https://github.com/mikemckiernan/sphinx-multiversion.git
 sphinxcontrib-copydirs@git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git
+fiddle

tests/integration/tf/retrieval/retrieval_config.py

@@ -0,0 +1,55 @@
+import fiddle as fdl
+
+from tests.integration.tf.retrieval.retrieval_utils import (
+    RetrievalTrainEvalRunner,
+    get_callbacks,
+    get_dataset,
+    get_dual_encoder_model,
+    get_item_frequencies,
+    get_loss,
+    get_metrics,
+    get_optimizer,
+    get_samplers,
+    get_schema,
+    get_youtube_dnn_model,
+)
+
+
+def config_retrieval_train_eval_runner(model_type, data_path):
+    def make_datasets(schema):
+        get_dataset_partial = fdl.Partial(get_dataset, schema)
+        train_ds = get_dataset_partial(dataset="train")
+        train_ds.data_path = schema.data_path
+        eval_ds = get_dataset_partial(dataset="valid")
+        eval_ds.data_path = schema.data_path
+        return train_ds, eval_ds
+
+    def make_model(schema, train_ds, model_type="two_tower"):
+        if model_type == "youtubednn":
+            model = fdl.Config(get_youtube_dnn_model, schema)
+        else:
+            samplers = fdl.Config(get_samplers, schema)
+            items_frequencies = fdl.Config(get_item_frequencies, schema, train_ds)
+            model = fdl.Config(get_dual_encoder_model, schema, samplers, items_frequencies)
+        return model
+
+    schema_cfg = fdl.Config(get_schema, data_path=data_path)
+    train_ds_cfg, eval_ds_cfg = make_datasets(schema_cfg)
+    model_cfg = make_model(schema_cfg, train_ds_cfg, model_type=model_type)
+    optimizer = fdl.Config(get_optimizer)
+    metrics = fdl.Config(get_metrics)
+    loss = fdl.Config(get_loss)
+    callbacks = fdl.Config(get_callbacks)
+
+    runner_cfg = fdl.Config(
+        RetrievalTrainEvalRunner,
+        schema=schema_cfg,
+        train_ds=train_ds_cfg,
+        eval_ds=eval_ds_cfg,
+        model=model_cfg,
+        optimizer=optimizer,
+        metrics=metrics,
+        loss=loss,
+        callbacks=callbacks,
+    )
+    return runner_cfg