Datasets:

---

Mlxa

/

bert-dataset

like 0

ComponentSpec:\n \"\"\"Returns double straight.\n Args:\n component: for cutback.\n cross_section: specification (CrossSection, string or dict).\n port1: name of first optical port.\n port2: name of second optical port.\n straight_length: length of straight.\n kwargs: cross_section settings.\n \"\"\"\n xs = gf.get_cross_section(cross_section, **kwargs)\n METHOD_NAME = gf.Component()\n straight_component = straight(\n length=straight_length or xs.radius * 2, cross_section=xs\n )\n straight_component2 = straight(\n length=straight_length or xs.radius * 2, cross_section=xs\n )\n straight_r = METHOD_NAME << straight_component\n straight_r2 = METHOD_NAME << straight_component2.mirror((1, 0))\n straight_r2 = straight_r2.move(\n origin=(0, 0),\n destination=(0, -component.ports[port1].y + component.ports[port2].y),\n )\n METHOD_NAME.add_port(\"o1\", port=straight_r.ports[\"o1\"])\n METHOD_NAME.add_port(\"o2\", port=straight_r2.ports[\"o1\"])\n METHOD_NAME.add_port(\"o3\", port=straight_r2.ports[\"o2\"])\n METHOD_NAME.add_port(\"o4\", port=straight_r.ports[\"o2\"])\n return METHOD_NAME"},"ids":{"kind":"list like","value":[9590,2152],"string":"[\n 9590,\n 2152\n]"}}},{"rowIdx":61,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self):\n \"\"\" BaseDirectory with no existence check accepts any pathlib path.\n \"\"\"\n foo = SimpleBaseDirectory()\n foo.path = pathlib.Path(\"!!!\")\n self.assertIsInstance(foo.path, str)"},"ids":{"kind":"list like","value":[9,53,1186,2147,11771],"string":"[\n 9,\n 53,\n 1186,\n 2147,\n 11771\n]"}}},{"rowIdx":62,"cells":{"text":{"kind":"string","value":"def METHOD_NAME():\n fmt = \"\"\"\n\t\t# comments are allowed\n\t\t> # big endian (see documentation for struct)\n\t\t# empty lines are allowed:\n\t\tashort: h\n\t\talong: l\n\t\tabyte: b\t# a byte\n\t\tachar: c\n\t\tastr: 5s\n\t\tafloat: f; adouble: d\t# multiple \"statements\" are allowed\n\t\tafixed: 16.16F\n\t\tabool: ?\n\t\tapad: x\n\t\"\"\"\n print(\"size:\", calcsize(fmt))\n class foo(object):\n pass\n i = foo()\n i.ashort = 0x7FFF\n i.along = 0x7FFFFFFF\n i.abyte = 0x7F\n i.achar = \"a\"\n i.astr = \"12345\"\n i.afloat = 0.5\n i.adouble = 0.5\n i.afixed = 1.5\n i.abool = True\n data = pack(fmt, i)\n print(\"data:\", repr(data))\n print(unpack(fmt, data))\n i2 = foo()\n unpack(fmt, data, i2)\n print(vars(i2))"},"ids":{"kind":"list like","value":[9],"string":"[\n 9\n]"}}},{"rowIdx":63,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(tmp_path):\n outfilename = tmp_path / \"vu_tide_hourly_p0.dfs0\"\n ds = mikeio.read(\"tests/testdata/vu_tide_hourly.dfs1\")\n assert ds.n_elements > 1\n ds_0 = ds.isel(0, axis=\"space\")\n assert ds_0.n_elements == 1\n ds_0_0 = ds_0.isel(0)\n assert ds_0_0.n_timesteps == 1\n ds_0_0.to_dfs(outfilename)\n dsnew = mikeio.read(outfilename)\n assert dsnew.n_timesteps == 1"},"ids":{"kind":"list like","value":[9,1472,1669,61,97,367,2085],"string":"[\n 9,\n 1472,\n 1669,\n 61,\n 97,\n 367,\n 2085\n]"}}},{"rowIdx":64,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self):\n self.window.show_all()\n self.window.present()"},"ids":{"kind":"list like","value":[697],"string":"[\n 697\n]"}}},{"rowIdx":65,"cells":{"text":{"kind":"string","value":"async def METHOD_NAME(\n auth: AcaPyAuth = Depends(acapy_auth),"},"ids":{"kind":"list like","value":[129,1837],"string":"[\n 129,\n 1837\n]"}}},{"rowIdx":66,"cells":{"text":{"kind":"string","value":"def METHOD_NAME():\n session = requests.Session()\n make_session_public_only(session, 'demo_domain', src="https://huggingface.co/datasets/Mlxa/testing")\n return session"},"ids":{"kind":"list like","value":[0,1,240],"string":"[\n 0,\n 1,\n 240\n]"}}},{"rowIdx":67,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self):\n self.assertEqual(build_password(\"plain\"), \"plaintext:plain\")"},"ids":{"kind":"list like","value":[9,235,11129],"string":"[\n 9,\n 235,\n 11129\n]"}}},{"rowIdx":68,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(\n user_id: str\n) -> List[learner_group_domain.LearnerGroup]:\n \"\"\"Returns a list of learner groups of the given facilitator.\n Args:\n user_id: str. The id of the facilitator.\n Returns:\n list(LearnerGroup). A list of learner groups of the given facilitator.\n \"\"\"\n learner_grp_models = (\n learner_group_models.LearnerGroupModel.get_by_facilitator_id(user_id))\n if not learner_grp_models:\n return []\n return [\n learner_group_services.get_learner_group_from_model(model)\n for model in learner_grp_models\n ]"},"ids":{"kind":"list like","value":[19,5916,861,47,-1],"string":"[\n 19,\n 5916,\n 861,\n 47,\n -1\n]"}}},{"rowIdx":69,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, value: Optional[float]) -> None:\n \"\"\"When not draining we pass thru to the socket,\n since when draining we control the timeout.\n \"\"\"\n if value is not None:\n self._recv_timeout_sec = value\n if self._drain_thread is None:\n socket.socket.METHOD_NAME(self, value)"},"ids":{"kind":"list like","value":[4247],"string":"[\n 4247\n]"}}},{"rowIdx":70,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(\n self,\n description: str,\n params: Mapping[str, Any],\n url: bool | None = False,\n provider: ExternalProviders | None = None,\n) -> str:\n if self.user:\n name = self.user.name or self.user.email\n else:\n name = \"Sentry\"\n issue_name = self.group.qualified_short_id or \"an issue\"\n if url and self.group.qualified_short_id:\n group_url = self.group.get_absolute_url(params={\"referrer\": \"activity_notification\"})\n issue_name = f\"{self.format_url(text=self.group.qualified_short_id, url=group_url, provider=provider)}\"\n context = {\"author\": name, \"an issue\": issue_name}\n context.update(params)\n return description.format(**context)"},"ids":{"kind":"list like","value":[1067,947,526],"string":"[\n 1067,\n 947,\n 526\n]"}}},{"rowIdx":71,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, native_face):\n self._face = native_face\n self._loops = [RhinoBrepLoop(loop) for loop in native_face.Loops]\n self._surface = RhinoNurbsSurface.from_rhino(self._face.UnderlyingSurface().ToNurbsSurface())"},"ids":{"kind":"list like","value":[0,4805],"string":"[\n 0,\n 4805\n]"}}},{"rowIdx":72,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, user):\n return self.get_for_user(user, teammembership__role=TeamMembership.ROLE.OWNER)"},"ids":{"kind":"list like","value":[19,2013,6969],"string":"[\n 19,\n 2013,\n 6969\n]"}}},{"rowIdx":73,"cells":{"text":{"kind":"string","value":"def METHOD_NAME():\n column = BigqueryColumn(\n name=\"date\",\n field_path=\"date\",\n ordinal_position=1,\n data_type=\"TIMESTAMP\",\n is_partition_column=True,\n cluster_column_position=None,\n comment=None,\n is_nullable=False,\n )\n partition_info = PartitionInfo(type=\"DAY\", field=\"date\", column=column)\n profiler = BigqueryProfiler(config=BigQueryV2Config(), report=BigQueryV2Report())\n test_table = BigqueryTable(\n name=\"test_table\",\n comment=\"test_comment\",\n rows_count=1,\n size_in_bytes=1,\n last_altered=datetime.now(timezone.utc),\n created=datetime.now(timezone.utc),\n partition_info=partition_info,\n max_partition_id=\"20200101\",\n )\n query = profiler.generate_partition_profiler_query(\n project=\"test_project\",\n schema=\"test_dataset\",\n table=test_table,\n )\n expected_query = \"\"\""},"ids":{"kind":"list like","value":[9,567,1724,1816,2312,7275,539],"string":"[\n 9,\n 567,\n 1724,\n 1816,\n 2312,\n 7275,\n 539\n]"}}},{"rowIdx":74,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, inputs, metric, functional_metric, ref_metric, ignore_index):\n \"\"\"Test functional implementation of metric.\"\"\"\n preds, target = inputs\n if ignore_index is not None:\n target = inject_ignore_index(target, ignore_index)\n self.run_functional_metric_test(\n preds=preds,\n target=target,\n metric_functional=functional_metric,\n reference_metric=partial(_sklearn_ranking, fn=ref_metric, ignore_index=ignore_index),\n metric_args={\n \"num_labels\": NUM_CLASSES,\n \"ignore_index\": ignore_index,\n },\n )"},"ids":{"kind":"list like","value":[9,9585,4510,4167],"string":"[\n 9,\n 9585,\n 4510,\n 4167\n]"}}},{"rowIdx":75,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, positions: TensorType[\"bs\":..., 3]) -> TensorType[\"bs\":..., 1]:\n \"\"\"Returns only the density. Used primarily with the density grid.\n Args:\n positions: the origin of the samples/frustums\n \"\"\"\n # Need to figure out a better way to descibe positions with a ray.\n ray_samples = RaySamples(\n frustums=Frustums(\n origins=positions,\n directions=torch.ones_like(positions),\n starts=torch.zeros_like(positions[..., :1]),\n ends=torch.zeros_like(positions[..., :1]),\n pixel_area=torch.ones_like(positions[..., :1]),\n )\n )\n density, _ = self.get_density(ray_samples)\n return density"},"ids":{"kind":"list like","value":[2915,667],"string":"[\n 2915,\n 667\n]"}}},{"rowIdx":76,"cells":{"text":{"kind":"string","value":" METHOD_NAME(self, old_name, new_name, merge=False):"},"ids":{"kind":"list like","value":[1887,2010],"string":"[\n 1887,\n 2010\n]"}}},{"rowIdx":77,"cells":{"text":{"kind":"string","value":"def METHOD_NAME():\n examinee = create_upgrade_pr(\n from_ref=cm.ComponentReference(\n name='c1',\n componentName='c1',\n version='1.2.3',\n ),\n to_ref=cm.ComponentReference(\n name='c1',\n componentName='c1',\n version='2.0.0',\n ),\n )\n cref = cm.ComponentReference(\n name='c1',\n componentName='c1',\n version='6.0.0',\n )\n reference_component = cm.Component(\n name='c1',\n version='6.6.6',\n repositoryContexts=(),\n provider=None,\n sources=(),\n resources=(),\n componentReferences=()\n )\n # test with reference component not declaring this dependency\n assert not examinee.is_obsolete(reference_component=reference_component)\n # add differently-named dependency with greater version\n reference_component.componentReferences = (\n dataclasses.replace(cref, componentName='other-name'),\n )\n assert not examinee.is_obsolete(reference_component=reference_component)\n # add same-named web dependency with lesser version\n reference_component.componentReferences = (\n dataclasses.replace(cref, version='0.0.1'),\n )\n assert not examinee.is_obsolete(reference_component=reference_component)\n # add same-named resource of greater version but different type\n # todo: we should actually also test dependencies towards resources of two different types\n reference_component.resources = (\n cm.Resource(\n name='c1',\n version='6.0.0',\n type=cm.ArtefactType.BLOB,\n access=None,\n ),\n )\n assert not examinee.is_obsolete(reference_component=reference_component)\n # finally, add greater dependency of matching type and name\n reference_component.componentReferences = (\n dataclasses.replace(cref, version='9.9.9'),\n )\n assert examinee.is_obsolete(reference_component=reference_component)"},"ids":{"kind":"list like","value":[9,137,8439],"string":"[\n 9,\n 137,\n 8439\n]"}}},{"rowIdx":78,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(testsystem_names, niterations=5):\n \"\"\"\n Run sampler stack on named test systems.\n Parameters\n ----------\n testsystem_names : list of str\n Names of test systems to run\n niterations : int, optional, default=5\n Number of iterations to run\n \"\"\"\n for testsystem_name in testsystem_names:\n import perses.tests.testsystems\n testsystem_class = getattr(perses.tests.testsystems, testsystem_name)\n # Instantiate test system.\n testsystem = testsystem_class()\n # Test MCMCSampler samplers.\n for environment in testsystem.environments:\n mcmc_sampler = testsystem.mcmc_samplers[environment]\n f = partial(mcmc_sampler.run, niterations)\n f.description = \"Testing MCMC sampler with %s '%s'\" % (testsystem_name, environment)\n yield f\n # Test ExpandedEnsembleSampler samplers.\n for environment in testsystem.environments:\n exen_sampler = testsystem.exen_samplers[environment]\n f = partial(exen_sampler.run, niterations)\n f.description = \"Testing expanded ensemble sampler with %s '%s'\" % (testsystem_name, environment)\n yield f\n # Test SAMSSampler samplers.\n for environment in testsystem.environments:\n sams_sampler = testsystem.sams_samplers[environment]\n f = partial(sams_sampler.run, niterations)\n f.description = \"Testing SAMS sampler with %s '%s'\" % (testsystem_name, environment)\n yield f\n # Test MultiTargetDesign sampler, if present.\n if hasattr(testsystem, 'designer') and (testsystem.designer is not None):\n f = partial(testsystem.designer.run, niterations)\n f.description = \"Testing MultiTargetDesign sampler with %s transfer free energy from vacuum -> %s\" % (testsystem_name, environment)\n yield f"},"ids":{"kind":"list like","value":[22,17407],"string":"[\n 22,\n 17407\n]"}}},{"rowIdx":79,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self) -> Response:\n \"\"\"\n Get a list with all of the tabels in TDEngine\n \"\"\"\n q = 'SHOW TABLES;'\n return self.native_query(q)"},"ids":{"kind":"list like","value":[19,2253],"string":"[\n 19,\n 2253\n]"}}},{"rowIdx":80,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(\n self,\n configs: List[Config[ModelConfig]],\n performances: List[Performance],\n) -> None:\n super().METHOD_NAME(configs, performances)\n # We need to sort by dataset to have the same ordering for each model config\n ordering = np.argsort([c.dataset.name() for c in configs])\n performance_df = Performance.to_dataframe(performances)\n # Extract all metrics\n metric_map = defaultdict(list)\n for i in ordering:\n metric_map[configs[i].model].append(\n performance_df.iloc[i][self.objectives].to_numpy(), # type: ignore\n )\n # Build the properties\n self.metrics = np.stack(list(metric_map.values()), axis=1)\n self.model_indices = {model: i for i, model in enumerate(metric_map)}\n # If we are in the multi-objective setting, we have to apply dataset-level quantile\n # normalization of each objective. Otherwise, we perform standardization.\n if not self.enforce_single_objective and len(self.objectives) > 1:\n transformer = QuantileTransformer(\n n_quantiles=min(1000, self.metrics.shape[0])\n )\n self.metrics = np.stack(\n [\n transformer.fit_transform(dataset_metrics)\n for dataset_metrics in self.metrics\n ]\n )\n else:\n transformer = StandardScaler()\n self.metrics = np.stack(\n [\n transformer.fit_transform(dataset_metrics)\n for dataset_metrics in self.metrics\n ]\n )"},"ids":{"kind":"list like","value":[90],"string":"[\n 90\n]"}}},{"rowIdx":81,"cells":{"text":{"kind":"string","value":"async def METHOD_NAME(mock_iam_client):\n group = await get_group(EXAMPLE_GROUPNAME, mock_iam_client)\n assert group[\"GroupName\"] == EXAMPLE_GROUPNAME"},"ids":{"kind":"list like","value":[9,19,846],"string":"[\n 9,\n 19,\n 846\n]"}}},{"rowIdx":82,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, Paramsmulticast):\n # controle parameters multicast\n return self.api.SetMulticastMultiSessionParameters(Paramsmulticast)"},"ids":{"kind":"list like","value":[5315,0,138,457,240,386],"string":"[\n 5315,\n 0,\n 138,\n 457,\n 240,\n 386\n]"}}},{"rowIdx":83,"cells":{"text":{"kind":"string","value":"f METHOD_NAME(self):"},"ids":{"kind":"list like","value":[9,356,171],"string":"[\n 9,\n 356,\n 171\n]"}}},{"rowIdx":84,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(m):\n opt = pyo.SolverFactory('gurobi')\n res = opt.solve(m)\n assert_optimal_termination(res)"},"ids":{"kind":"list like","value":[283,5295,708],"string":"[\n 283,\n 5295,\n 708\n]"}}},{"rowIdx":85,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self) -> str:\n \"\"\"\n Resource ID.\n \"\"\"\n return pulumi.get(self, \"id\")"},"ids":{"kind":"list like","value":[147],"string":"[\n 147\n]"}}},{"rowIdx":86,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self):\n return self.event.METHOD_NAME + f\"/session/{self.id}\""},"ids":{"kind":"list like","value":[1055,548],"string":"[\n 1055,\n 548\n]"}}},{"rowIdx":87,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(colorer, s, i):\n return colorer.match_seq_regexp(s, i, kind=\"label\", regexp=\"`[A-z0-9]+[^`]+`_{1,2}\")"},"ids":{"kind":"list like","value":[3183,6935],"string":"[\n 3183,\n 6935\n]"}}},{"rowIdx":88,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self):\n for pos in self:\n seq = pos.l10n_es_simplified_invoice_sequence_id\n pos.l10n_es_simplified_invoice_number = (\n seq._get_current_sequence().number_next_actual\n )\n pos.l10n_es_simplified_invoice_prefix = seq._get_prefix_suffix()[0]\n pos.l10n_es_simplified_invoice_padding = seq.padding"},"ids":{"kind":"list like","value":[226,8018,2486,771],"string":"[\n 226,\n 8018,\n 2486,\n 771\n]"}}},{"rowIdx":89,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self):\n x = tensor.Tensor(np.array([1, 2, 3]))\n self.assertEqual(x.rank, 1)"},"ids":{"kind":"list like","value":[9,1499,137,206,43,798],"string":"[\n 9,\n 1499,\n 137,\n 206,\n 43,\n 798\n]"}}},{"rowIdx":90,"cells":{"text":{"kind":"string","value":"def METHOD_NAME():\n assert not np.isnan(atmosphere.get_relative_airmass(10))"},"ids":{"kind":"list like","value":[9,10054,1997],"string":"[\n 9,\n 10054,\n 1997\n]"}}},{"rowIdx":91,"cells":{"text":{"kind":"string","value":"def METHOD_NAME():\n \"\"\"Return the default filters (all available filters).\"\"\"\n return dict((name, set(PlayerIter(name))) for name in PlayerIter.filters)"},"ids":{"kind":"list like","value":[19,235,469],"string":"[\n 19,\n 235,\n 469\n]"}}},{"rowIdx":92,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, token_ids: Sequence[bytes]) -> Sequence[KlerosToken]:\n queries = []\n for token_id in token_ids:\n queries.append(self.kleros_contract.functions.getTokenInfo(token_id))\n # name string, ticker string, addr address, symbolMultihash string, status uint8, numberOfRequests uint256\n token_infos = self.ethereum_client.batch_call(queries)\n return [KlerosToken(*token_info) for token_info in token_infos]"},"ids":{"kind":"list like","value":[19,466,100],"string":"[\n 19,\n 466,\n 100\n]"}}},{"rowIdx":93,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(\n self, aligned_segment_starting_times: List[List[float]], stub_test: bool = False\n):\n \"\"\"\n Align the individual starting time for each video in this interface relative to the common session start time.\n Must be in units seconds relative to the common 'session_start_time'.\n Parameters\n ----------\n aligned_segment_starting_times : list of list of floats\n The relative starting times of each video.\n Outer list is over file paths (readers).\n Inner list is over segments of each recording.\n \"\"\"\n number_of_files_from_starting_times = len(aligned_segment_starting_times)\n assert number_of_files_from_starting_times == len(self.readers_list), (\n f\"The length of the outer list of 'starting_times' ({number_of_files_from_starting_times}) \"\n \"does not match the number of files ({len(self.readers_list)})!\"\n )\n for file_index, (reader, aligned_segment_starting_times_by_file) in enumerate(\n zip(self.readers_list, aligned_segment_starting_times)\n ):\n number_of_segments = reader.header[\"nb_segment\"][0]\n assert number_of_segments == len(\n aligned_segment_starting_times_by_file\n ), f\"The length of starting times index {file_index} does not match the number of segments of that reader!\"\n reader._t_starts = aligned_segment_starting_times_by_file"},"ids":{"kind":"list like","value":[0,7546,4373,8466,3148],"string":"[\n 0,\n 7546,\n 4373,\n 8466,\n 3148\n]"}}},{"rowIdx":94,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, msg):\n pass"},"ids":{"kind":"list like","value":[69,5862],"string":"[\n 69,\n 5862\n]"}}},{"rowIdx":95,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(self, output, identifier):\n return self._wrapped.METHOD_NAME(output._lines, identifier)"},"ids":{"kind":"list like","value":[19,99,280,146],"string":"[\n 19,\n 99,\n 280,\n 146\n]"}}},{"rowIdx":96,"cells":{"text":{"kind":"string","value":"def METHOD_NAME():\n x = np.zeros((5, 5), dtype=int)\n array_2d_view_assign(x[::, ::], 9)\n array_2d_view_assign(x[:2:2, :2:3], 10)\n array_2d_view_assign(x[3::2, 3::3], 11)\n array_2d_view_assign(x[1:2, 2:3], 12)\n array_1d_view_assign(x[0, :], 1)\n array_1d_view_assign(x[1, ::2], 2)\n array_1d_view_assign(x[2, 1:4:2], 3)\n array_1d_view_assign(x[3, 3:4], 4)\n array_1d_view_assign(x[:, 0], 5)\n array_1d_view_assign(x[::2, 1], 6)\n array_1d_view_assign(x[1:4:2, 2], 7)\n array_1d_view_assign(x[3:4, 3], 8)\n for i in range(np.shape(x)[0]):\n for j in range(np.shape(x)[1]):\n print(x[i][j])"},"ids":{"kind":"list like","value":[877,1085,1179],"string":"[\n 877,\n 1085,\n 1179\n]"}}},{"rowIdx":97,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(iterable):\n \"\"\"Test whether visitors properly set the type constraint of the a For node representing for/else statement\n iterating over a heterogeneous list.\n \"\"\"\n assume(type(iterable[0]) != type(iterable[1]))\n val_types = [type(val) for val in iterable]\n if int in val_types:\n assume(bool not in val_types)\n if bool in val_types:\n assume(int not in val_types)\n program = f\"for elt in {iterable}:\\n\" f\" x = elt\\n\"\n module, TypeInferrer = cs._parse_text(program)\n for_node = list(module.nodes_of_class(nodes.For))[0]\n local_type_var = module.type_environment.lookup_in_env(\"x\")\n inferred_type = TypeInferrer.type_constraints.resolve(local_type_var).getValue()\n assert inferred_type == Any"},"ids":{"kind":"list like","value":[9,43,5565,245],"string":"[\n 9,\n 43,\n 5565,\n 245\n]"}}},{"rowIdx":98,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(plistpath, content):\n \"\"\"A test utility to create a plist file with known content.\n Ensures that the directory for the file exists, and writes an XML plist with\n specific content.\n :param plistpath: The path for the plist file to create.\n :param content: A dictionary of content that plistlib can use to create the plist\n file.\n :returns: The path to the file that was created.\n \"\"\"\n plistpath.parent.mkdir(parents=True, exist_ok=True)\n with plistpath.open(\"wb\") as f:\n plistlib.dump(content, f)\n return plistpath"},"ids":{"kind":"list like","value":[129,5953,171],"string":"[\n 129,\n 5953,\n 171\n]"}}},{"rowIdx":99,"cells":{"text":{"kind":"string","value":"def METHOD_NAME(instance, check, aggregator):\n del instance['custom_queries']\n with mock.patch(\n 'datadog_checks.ibm_was.IbmWasCheck.make_request', return_value=mock_data('perfservlet-multiple-nodes.xml')\n ):\n check = check(instance)\n check.check(instance)\n node = 'node:cmhqlvij2a04'\n for metric_name, metrics in aggregator._metrics.items():\n for metric in metrics:\n if 'server:IJ2Server02' in metric.tags:\n assert node in metric.tags, \"Expected '{}' tag in '{}' tags, found {}\".format(\n node, metric_name, metric.tags\n )"},"ids":{"kind":"list like","value":[9,2786,163,82],"string":"[\n 9,\n 2786,\n 163,\n 82\n]"}}}],"truncated":true},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":270000,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc1NzQ1MzM0MSwic3ViIjoiL2RhdGFzZXRzL01seGEvYmVydC1kYXRhc2V0IiwiZXhwIjoxNzU3NDU2OTQxLCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.Ag-_3GTUSEK4-Czs31nFMOW3aQb6QoTMoP1LulHat_U_f8S93a1pGb29tnNqIl_wNOYCDovOOdDitSB0fPvrCQ","displayUrls":true},"dataset":"Mlxa/bert-dataset","isGated":false,"isPrivate":false,"hasParquetFormat":true,"author":{"_id":"64ac5b142af65e43e6f6084a","avatarUrl":"/avatars/487ac751ccd971428a47de8ad35dc79a.svg","fullname":"Mikhail Budnikov","name":"Mlxa","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":6},"compact":true}">