chore(api): remove batch inference (#3261)

# What does this PR do?

APIs removed:
 - POST /v1/batch-inference/completion
 - POST /v1/batch-inference/chat-completion
 - POST /v1/inference/batch-completion
 - POST /v1/inference/batch-chat-completion

note -
- batch-completion & batch-chat-completion were only implemented for
inference=inline::meta-reference
 - batch-inference were not implemented

tests/integration/inference/test_batch_inference.py

@@ -1,76 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-
-import pytest
-
-from ..test_cases.test_case import TestCase
-
-
-def skip_if_provider_doesnt_support_batch_inference(client_with_models, model_id):
-    models = {m.identifier: m for m in client_with_models.models.list()}
-    models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
-    provider_id = models[model_id].provider_id
-    providers = {p.provider_id: p for p in client_with_models.providers.list()}
-    provider = providers[provider_id]
-    if provider.provider_type not in ("inline::meta-reference",):
-        pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support batch inference")
-
-
-@pytest.mark.parametrize(
-    "test_case",
-    [
-        "inference:completion:batch_completion",
-    ],
-)
-def test_batch_completion_non_streaming(client_with_models, text_model_id, test_case):
-    skip_if_provider_doesnt_support_batch_inference(client_with_models, text_model_id)
-    tc = TestCase(test_case)
-
-    content_batch = tc["contents"]
-    response = client_with_models.inference.batch_completion(
-        content_batch=content_batch,
-        model_id=text_model_id,
-        sampling_params={
-            "max_tokens": 50,
-        },
-    )
-    assert len(response.batch) == len(content_batch)
-    for i, r in enumerate(response.batch):
-        print(f"response {i}: {r.content}")
-        assert len(r.content) > 10
-
-
-@pytest.mark.parametrize(
-    "test_case",
-    [
-        "inference:chat_completion:batch_completion",
-    ],
-)
-def test_batch_chat_completion_non_streaming(client_with_models, text_model_id, test_case):
-    skip_if_provider_doesnt_support_batch_inference(client_with_models, text_model_id)
-    tc = TestCase(test_case)
-    qa_pairs = tc["qa_pairs"]
-
-    message_batch = [
-        [
-            {
-                "role": "user",
-                "content": qa["question"],
-            }
-        ]
-        for qa in qa_pairs
-    ]
-
-    response = client_with_models.inference.batch_chat_completion(
-        messages_batch=message_batch,
-        model_id=text_model_id,
-    )
-    assert len(response.batch) == len(qa_pairs)
-    for i, r in enumerate(response.batch):
-        print(f"response {i}: {r.completion_message.content}")
-        assert len(r.completion_message.content) > 0
-        assert qa_pairs[i]["answer"].lower() in r.completion_message.content.lower()