Fix model loading issues and forecast_naive slicing bug in TimesFM 2.5

- Allow model wrapper constructors (__init__) to accept and ignore extra keyword arguments (e.g. proxies) passed by huggingface_hub during from_pretrained.
- Implement load_checkpoint for TimesFM_2p5_200M_torch and TimesFM_2p5_200M_flax to restore weights from local paths.
- Fix slicing bug in PyTorch's forecast_naive to correctly slice the time/horizon dimension ([:, :horizon, :]) instead of quantiles.
- Add unit tests in tests/test_model_loading.py covering local checkpoint loading, hub compatibility, and prediction shape correctness.

tests/test_model_loading.py

@@ -0,0 +1,68 @@
+# Copyright 2025 Google LLC
+#
+# 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.
+
+"""Tests for loading TimesFM 2.5 models."""
+
+import os
+import tempfile
+
+from timesfm.timesfm_2p5.timesfm_2p5_torch import TimesFM_2p5_200M_torch
+from timesfm.timesfm_2p5.timesfm_2p5_flax import TimesFM_2p5_200M_flax
+
+
+class TestModelLoading:
+  """Tests to verify model instantiation, loading, and compatibility."""
+
+  def test_torch_load_checkpoint_and_from_pretrained_local(self):
+    """Verifies that PyTorch load_checkpoint and from_pretrained work locally."""
+    # 1. Instantiate the model wrapper with compilation disabled
+    tfm = TimesFM_2p5_200M_torch(torch_compile=False)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+      # 2. Save the model's randomly-initialized weights
+      tfm._save_pretrained(tmpdir)
+
+      # Verify weights file is written
+      weights_path = os.path.join(tmpdir, "model.safetensors")
+      assert os.path.exists(weights_path)
+
+      # 3. Verify that load_checkpoint works from the temp directory path
+      tfm2 = TimesFM_2p5_200M_torch(torch_compile=False)
+      tfm2.load_checkpoint(tmpdir, torch_compile=False)
+
+      # 4. Verify that from_pretrained works with a local directory path
+      # and accepts/ignores extra kwargs (like proxies) without raising TypeError
+      tfm3 = TimesFM_2p5_200M_torch.from_pretrained(
+          tmpdir,
+          torch_compile=False,
+          proxies={"http": "http://dummy.proxy"},
+          custom_kwarg="dummy_value",
+      )
+      assert tfm3 is not None
+      assert not tfm3.torch_compile
+
+      # 5. Run a simple prediction step to verify the loaded model performs forward pass
+      import numpy as np
+      inputs = [np.random.randn(32)]
+      forecasts = tfm3.model.forecast_naive(horizon=10, inputs=inputs)
+      assert len(forecasts) == 1
+      assert forecasts[0].shape == (10, 10)
+
+  def test_flax_model_init_kwargs(self):
+    """Verifies that Flax model wrapper constructor accepts arbitrary kwargs."""
+    tfm = TimesFM_2p5_200M_flax(
+        proxies={"http": "http://dummy.proxy"},
+        custom_kwarg="dummy_value",
+    )
+    assert tfm is not None