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code-code-galeras-code-completion-from-docstring-3k-deduped

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Semeru Lab 7

{ "docstring": "Return the list of available hvac operation modes.\n\n HEAT and COOL mode are exclusive. End user has to enable a mode manually within the Somfy application.\n So only one mode can be displayed. Auto mode is a scheduler.\n ", "language": "en", "n_whitespaces": 59, "n_words": 38, "vocab_size": 33 }

{ "docstring": "Helper function that asserts `TaskInstances` of a given `task_id` are in a given state.", "language": "en", "n_whitespaces": 13, "n_words": 14, "vocab_size": 12 }

{ "docstring": "\n\t\t\tSELECT parent FROM `tabDynamic Link`\n\t\t\tWHERE\n\t\t\t\tparenttype = 'Contact' AND\n\t\t\t\tparentfield = 'links' AND\n\t\t\t\tlink_doctype = 'Customer' AND\n\t\t\t\tlink_name = %s\n\t\t\t", "language": "en", "n_whitespaces": 15, "n_words": 21, "vocab_size": 16 }

{ "docstring": "\n This posts to the unpublish view and checks that the page was unpublished but its descendants were not\n ", "language": "en", "n_whitespaces": 33, "n_words": 18, "vocab_size": 17 }

{ "docstring": "\n The function read_json accepts three input types:\n 1. filepath (string-like)\n 2. file-like object (e.g. open file object, StringIO)\n 3. JSON string\n\n This method turns (1) into (2) to simplify the rest of the processing.\n It returns input types (2) and (3) unchanged.\n\n It raises FileNotFoundError if the input is a string ending in\n one of .json, .json.gz, .json.bz2, etc. but no such file exists.\n ", "language": "en", "n_whitespaces": 140, "n_words": 64, "vocab_size": 55 }

{ "docstring": "\nimport runpy\nimport sys\nsys.path = {additional_paths or []} + sys.path\nsys.argv[1:] = {args}\nrunpy.run_module(\"pip\", run_name=\"__main__\", alter_sys=True)\n", "language": "en", "n_whitespaces": 12, "n_words": 17, "vocab_size": 14 }

{ "docstring": "\n Test that pprint works for heap allocated types.\n ", "language": "en", "n_whitespaces": 15, "n_words": 8, "vocab_size": 8 }

{ "docstring": "\n Test that contains a field in the `select` that is performance related but currently\n not supported should return a 400\n ", "language": "en", "n_whitespaces": 42, "n_words": 20, "vocab_size": 18 }

{ "docstring": "Analyze the given list of integration test targets and return a dictionary expressing target names and the targets on which they depend.", "language": "en", "n_whitespaces": 21, "n_words": 22, "vocab_size": 19 }

{ "docstring": "Get a short text description of the error.\n\n This is a string suitable to show to the user as error message.\n ", "language": "en", "n_whitespaces": 35, "n_words": 21, "vocab_size": 18 }

{ "docstring": "Test controller is able to recover starting -> running replicas from\n actor names.\n ", "language": "en", "n_whitespaces": 19, "n_words": 13, "vocab_size": 13 }

{ "docstring": "Return the list of entities that were derived from this endpoint.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": "Returns the value of the header matching *name*.\n\n If there are multiple matching headers, the values are\n combined into a single string separated by commas and spaces.\n\n If no matching header is found, returns *default* or None if\n the *default* is not specified.\n\n If the headers are unknown, raises http.client.ResponseNotReady.\n\n ", "language": "en", "n_whitespaces": 92, "n_words": 50, "vocab_size": 37 }

{ "docstring": "Assert that generate_pipeline_code() returns the correct code given a specific pipeline with two CombineDFs.make_pipeline(\n make_union(\n StackingEstimator(estimator=GradientBoostingClassifier(learning_rate=38.0, max_depth=5, max_features=5, min_samples_leaf=5, min_samples_split=0.05, n_estimators=0.5)),\n make_union(\n MinMaxScaler(),\n make_pipeline(\n MaxAbsScaler(),\n ZeroCount()\n )\n )\n ),\n KNeighborsClassifier(n_neighbors=18, p=\"uniform\", weights=2)\n)", "language": "en", "n_whitespaces": 124, "n_words": 33, "vocab_size": 30 }

{ "docstring": "Create the fully qualified name of the files created by\n {console,gui}_scripts for the given ``dist``.\n Returns the list of file names\n ", "language": "en", "n_whitespaces": 30, "n_words": 21, "vocab_size": 17 }

{ "docstring": "Get bbox centers from boxes.\n Args:\n boxes (Tensor): boxes with shape (..., 4), \"xmin, ymin, xmax, ymax\" format.\n Returns:\n Tensor: boxes centers with shape (..., 2), \"cx, cy\" format.\n ", "language": "en", "n_whitespaces": 52, "n_words": 29, "vocab_size": 22 }

{ "docstring": "\n Args:\n input (str) or (list): video file path or image data list\n Returns:\n results (dict): \n ", "language": "en", "n_whitespaces": 60, "n_words": 15, "vocab_size": 14 }

{ "docstring": "\n Recursive deconstruction for a field and its arguments.\n Used for full comparison for rename/alter; sometimes a single-level\n deconstruction will not compare correctly.\n ", "language": "en", "n_whitespaces": 51, "n_words": 22, "vocab_size": 18 }

{ "docstring": "Test that cancellation of the new `Deferred` leaves the original running.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 10 }

{ "docstring": "\n Get the mapping of generator local variables to their current values.\n\n A dict is returned, with the keys the local variable names and values the\n bound values.", "language": "en", "n_whitespaces": 36, "n_words": 27, "vocab_size": 22 }

{ "docstring": "The last bin contains the end date, so its own end could be after the history end", "language": "en", "n_whitespaces": 16, "n_words": 17, "vocab_size": 14 }

{ "docstring": "Called on __init()__ to validate trainer attributes.", "language": "en", "n_whitespaces": 6, "n_words": 7, "vocab_size": 7 }

{ "docstring": "Return longitude if provided in extra_state_attributes or None.", "language": "en", "n_whitespaces": 7, "n_words": 8, "vocab_size": 8 }

{ "docstring": "\n :return: cache directory tied to the user (if opinionated with ``Cache`` folder within ``$appname``) e.g.\n ``%USERPROFILE%\\\\AppData\\\\Local\\\\$appauthor\\\\$appname\\\\Cache\\\\$version``\n ", "language": "en", "n_whitespaces": 39, "n_words": 16, "vocab_size": 16 }

{ "docstring": "Constructs the ProjectConfig information.\n :param project: The project to load configuration for. Ensure that\n organization is bound on this object; otherwise it will be loaded from\n the database.\n :param full_config: True if only the full config is required, False\n if only the restricted (for external relays) is required\n (default True, i.e. full configuration)\n :param project_keys: Pre-fetched project keys for performance. However, if\n no project keys are provided it is assumed that the config does not\n need to contain auth information (this is the case when used in\n python's StoreView)\n :return: a ProjectConfig object for the given project\n ", "language": "en", "n_whitespaces": 161, "n_words": 97, "vocab_size": 71 }

{ "docstring": "\n Execute a single SPARQL query on the knowledge graph to retrieve an answer and unpack\n different answer styles for boolean queries, count queries, and list queries.\n\n :param sparql_query: SPARQL query that shall be executed on the knowledge graph\n ", "language": "en", "n_whitespaces": 67, "n_words": 38, "vocab_size": 29 }

{ "docstring": "Get the finder for the path entry from sys.path_importer_cache.\n\n If the path entry is not in the cache, find the appropriate finder\n and cache it. If no finder is available, store None.\n\n ", "language": "en", "n_whitespaces": 53, "n_words": 32, "vocab_size": 22 }

{ "docstring": "\n Django shouldn't break the default role for interpreted text\n when ``publish_parts`` is used directly, by setting it to\n ``cmsreference`` (#6681).\n ", "language": "en", "n_whitespaces": 49, "n_words": 20, "vocab_size": 20 }

{ "docstring": "\n Test to make sure only backfill points that are older and come before\n the `current_depth` are returned.\n ", "language": "en", "n_whitespaces": 39, "n_words": 17, "vocab_size": 16 }

{ "docstring": "\n Convert a SymPy expression into a Theano graph variable.\n\n .. deprecated:: 1.8\n\n ``sympy.printing.theanocode`` is deprecated. Theano has been renamed to\n Aesara. Use ``sympy.printing.aesaracode`` instead. See\n :ref:`theanocode-deprecated` for more information.\n\n Parameters\n ==========\n\n expr : sympy.core.expr.Expr\n SymPy expression object to convert.\n\n cache : dict\n Cached Theano variables (see :class:`TheanoPrinter.cache\n <TheanoPrinter>`). Defaults to the module-level global cache.\n\n dtypes : dict\n Passed to :meth:`.TheanoPrinter.doprint`.\n\n broadcastables : dict\n Passed to :meth:`.TheanoPrinter.doprint`.\n\n Returns\n =======\n\n theano.gof.graph.Variable\n A variable corresponding to the expression's value in a Theano symbolic\n expression graph.\n\n \n sympy.printing.theanocode is deprecated. Theano has been renamed to\n Aesara. Use sympy.printing.aesaracode instead.", "language": "en", "n_whitespaces": 209, "n_words": 94, "vocab_size": 63 }

{ "docstring": "Validate that labels are split correctly if split is known.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 9 }

{ "docstring": "\n Test get_name with a compatibility SID. Should return `None` as we want to\n ignore these SIDs\n ", "language": "en", "n_whitespaces": 26, "n_words": 16, "vocab_size": 16 }

{ "docstring": "Returns an iterable of entrypoints.\n\n For compatibility with Python 3.8/3.9.\n In 3.10 the return type changed from a dict to an ``importlib.metadata.EntryPoints``.\n This compatibility utility can be removed once Python 3.10 is the minimum.\n ", "language": "en", "n_whitespaces": 46, "n_words": 34, "vocab_size": 29 }

{ "docstring": "\n :returns: If the private key is an elliptic key, the name of its curve.\n :rtype: str\n ", "language": "en", "n_whitespaces": 38, "n_words": 16, "vocab_size": 15 }

{ "docstring": "\n A human-readable version of this page's type\n ", "language": "en", "n_whitespaces": 22, "n_words": 7, "vocab_size": 7 }

{ "docstring": "\n Render the plot and write it to a buffer or file on disk.\n\n Parameters\n ----------\n fname : str, path, or buffer\n Location on disk to save the figure, or a buffer to write into.\n Other keyword arguments are passed to :meth:`matplotlib.figure.Figure.savefig`.\n\n ", "language": "en", "n_whitespaces": 95, "n_words": 41, "vocab_size": 30 }

{ "docstring": "\n If the link is a candidate for install, convert it to an\n InstallationCandidate and return it. Otherwise, return None.\n ", "language": "en", "n_whitespaces": 41, "n_words": 19, "vocab_size": 18 }

{ "docstring": "\n Vertical bar plot.\n\n A bar plot is a plot that presents categorical data with\n rectangular bars with lengths proportional to the values that they\n represent. A bar plot shows comparisons among discrete categories. One\n axis of the plot shows the specific categories being compared, and the\n other axis represents a measured value.\n ", "language": "en", "n_whitespaces": 102, "n_words": 52, "vocab_size": 38 }

{ "docstring": "Decodes the output of a softmax.\n\n Can use either greedy search (also known as best path)\n or a constrained dictionary search.\n\n Args:\n y_pred: tensor `(samples, time_steps, num_categories)`\n containing the prediction, or output of the softmax.\n input_length: tensor `(samples, )` containing the sequence length for\n each batch item in `y_pred`.\n greedy: perform much faster best-path search if `true`.\n This does not use a dictionary.\n beam_width: if `greedy` is `false`: a beam search decoder will be used\n with a beam of this width.\n top_paths: if `greedy` is `false`,\n how many of the most probable paths will be returned.\n\n Returns:\n Tuple:\n List: if `greedy` is `true`, returns a list of one element that\n contains the decoded sequence.\n If `false`, returns the `top_paths` most probable\n decoded sequences.\n Each decoded sequence has shape (samples, time_steps).\n Important: blank labels are returned as `-1`.\n Tensor `(top_paths, )` that contains\n the log probability of each decoded sequence.\n ", "language": "en", "n_whitespaces": 373, "n_words": 149, "vocab_size": 99 }

{ "docstring": "\n Start displaying the progress `bar` in a notebook.\n\n Parameters\n ----------\n bar : tqdm.tqdm\n The progress bar wrapper to display in a notebook cell.\n ", "language": "en", "n_whitespaces": 46, "n_words": 23, "vocab_size": 19 }

{ "docstring": "\n Remove specified fileset(s)/rpm package(s).\n\n name\n The name of the fileset or rpm package to be deleted.\n\n .. versionadded:: 3005\n\n preference to install rpm packages are to use in the following order:\n /opt/freeware/bin/dnf\n /opt/freeware/bin/yum\n /usr/bin/yum\n /usr/bin/rpm\n\n Multiple Package Options:\n\n pkgs\n A list of filesets and/or rpm packages to delete.\n Must be passed as a python list. The ``name`` parameter will be\n ignored if this option is passed.\n\n\n Returns a list containing the removed packages.\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' pkg.remove <fileset/rpm package name>\n salt '*' pkg.remove tcsh\n salt '*' pkg.remove xlC.rte\n salt '*' pkg.remove Firefox.base.adt\n salt '*' pkg.remove pkgs='[\"foo\", \"bar\"]'\n ", "language": "en", "n_whitespaces": 243, "n_words": 101, "vocab_size": 72 }

{ "docstring": "\n Convert Gregorian float of the date, preserving hours, minutes,\n seconds and microseconds. Return value is a `.datetime`.\n\n The input date *x* is a float in ordinal days at UTC, and the output will\n be the specified `.datetime` object corresponding to that time in\n timezone *tz*, or if *tz* is ``None``, in the timezone specified in\n :rc:`timezone`.\n ", "language": "en", "n_whitespaces": 79, "n_words": 56, "vocab_size": 43 }

{ "docstring": "Get the documentation string for an object.\n\n All tabs are expanded to spaces. To clean up docstrings that are\n indented to line up with blocks of code, any whitespace than can be\n uniformly removed from the second line onwards is removed.", "language": "en", "n_whitespaces": 50, "n_words": 41, "vocab_size": 36 }

{ "docstring": "Takes a marshmallow class and dict of parameter values and appropriately instantiantes the schema.", "language": "en", "n_whitespaces": 13, "n_words": 14, "vocab_size": 13 }

{ "docstring": "Ensure that the refund method is called when the refund process\n is already ongoing but not covered full payment captured amount.", "language": "en", "n_whitespaces": 23, "n_words": 21, "vocab_size": 18 }

{ "docstring": "\n Reconstruct a list of all entrypoints from the groups.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }

{ "docstring": " Read all lines available on the input stream\n and return them as a list.\n\n Line breaks are implemented using the codec's decoder\n method and are included in the list entries.\n\n sizehint, if given, is ignored since there is no efficient\n way to finding the true end-of-line.\n\n ", "language": "en", "n_whitespaces": 109, "n_words": 46, "vocab_size": 40 }

{ "docstring": "\n Notify clients about events statuses in the queue periodically.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Read the .pyi file if present and scan for dependencies.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n Perform a topological sort of the graph.\n :return: A tuple, the first element of which is a topologically sorted\n list of distributions, and the second element of which is a\n list of distributions that cannot be sorted because they have\n circular dependencies and so form a cycle.\n ", "language": "en", "n_whitespaces": 117, "n_words": 47, "vocab_size": 32 }

{ "docstring": "Clears rewards and metrics, but retains RNN and other state.\n\n This is used to carry state across multiple logical episodes in the\n same env (i.e., if `soft_horizon` is set).\n ", "language": "en", "n_whitespaces": 50, "n_words": 29, "vocab_size": 27 }

{ "docstring": "\n Returns the proper model field name corresponding to the given\n field_name to use for ordering. field_name may either be the name of a\n proper model field or the name of a method (on the admin or model) or a\n callable with the 'admin_order_field' attribute. Returns None if no\n proper model field name can be matched.\n ", "language": "en", "n_whitespaces": 98, "n_words": 55, "vocab_size": 32 }

{ "docstring": "Check that ordering refers to existing fields or is random.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Returns object types that this class should be able to `parse` recusrively.\n The order of the objects indicates the order in which the parser should\n try to parse each subitem.\n :returns: A list of Parsable classes.\n :rtype list:\n ", "language": "en", "n_whitespaces": 73, "n_words": 38, "vocab_size": 32 }

{ "docstring": "\n Cast an expression inplace.\n\n Parameters\n ----------\n terms : Op\n The expression that should cast.\n acceptable_dtypes : list of acceptable numpy.dtype\n Will not cast if term's dtype in this list.\n dtype : str or numpy.dtype\n The dtype to cast to.\n ", "language": "en", "n_whitespaces": 82, "n_words": 39, "vocab_size": 31 }

{ "docstring": "\n Steps:\n 1. Find all org integrations that match the external id\n 2. Search the configs to find one that matches the vercel project of the webhook\n 3. Look up the Sentry project that matches\n 4. Look up the connected internal integration\n 5. Find the token associated with that installation\n 6. Determine the commit sha and repo based on what provider is used\n 7. Create the release using the token WITHOUT refs\n 8. Update the release with refs\n ", "language": "en", "n_whitespaces": 180, "n_words": 77, "vocab_size": 55 }

{ "docstring": "The user, if they are represented persistently in our app.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n Create a new `.AxesLocator` for the specified cell.\n\n Parameters\n ----------\n nx, nx1 : int\n Integers specifying the column-position of the\n cell. When *nx1* is None, a single *nx*-th column is\n specified. Otherwise, location of columns spanning between *nx*\n to *nx1* (but excluding *nx1*-th column) is specified.\n ", "language": "en", "n_whitespaces": 126, "n_words": 46, "vocab_size": 37 }

{ "docstring": "This test verifies the accuracy of construction of Zulip's edit\n history data structures.", "language": "en", "n_whitespaces": 19, "n_words": 13, "vocab_size": 12 }

{ "docstring": "(`tabProject`.customer = %s or\n\t\t\tifnull(`tabProject`.customer,\"\")=\"\") andselect {fields} from `tabProject`\n\t\twhere\n\t\t\t`tabProject`.status not in ('Completed', 'Cancelled')\n\t\t\tand {cond} {scond} {match_cond}\n\t\torder by\n\t\t\t(case when locate(%(_txt)s, `tabProject`.name) > 0 then locate(%(_txt)s, `tabProject`.name) else 99999 end),\n\t\t\t`tabProject`.idx desc,\n\t\t\t`tabProject`.name asc\n\t\tlimit {page_len} offset {start}", "language": "en", "n_whitespaces": 31, "n_words": 41, "vocab_size": 39 }

{ "docstring": "\n test that autosuggest is only applied at end of line.\n ", "language": "en", "n_whitespaces": 17, "n_words": 10, "vocab_size": 10 }

{ "docstring": "A set of stacked residual blocks.\n\n Args:\n x: input tensor.\n filters: integer, filters of the bottleneck layer in a block.\n blocks: integer, blocks in the stacked blocks.\n stride1: default 2, stride of the first layer in the first block.\n groups: default 32, group size for grouped convolution.\n name: string, stack label.\n\n Returns:\n Output tensor for the stacked blocks.\n ", "language": "en", "n_whitespaces": 102, "n_words": 58, "vocab_size": 40 }

{ "docstring": "\n Check if the Index holds data with mixed data types.\n\n Returns\n -------\n bool\n Whether or not the Index holds data with mixed data types.\n\n See Also\n --------\n is_boolean : Check if the Index only consists of booleans.\n is_integer : Check if the Index only consists of integers.\n is_floating : Check if the Index is a floating type.\n is_numeric : Check if the Index only consists of numeric data.\n is_object : Check if the Index is of the object dtype.\n is_categorical : Check if the Index holds categorical data.\n is_interval : Check if the Index holds Interval objects.\n\n Examples\n --------\n >>> idx = pd.Index(['a', np.nan, 'b'])\n >>> idx.is_mixed()\n True\n\n >>> idx = pd.Index([1.0, 2.0, 3.0, 5.0])\n >>> idx.is_mixed()\n False\n ", "language": "en", "n_whitespaces": 284, "n_words": 118, "vocab_size": 56 }

{ "docstring": "\n Fixture that starts a local http server in a separate process on localhost\n and returns the port.\n\n Running in a separate process instead of a thread to allow termination/killing\n of http server upon cleanup.\n ", "language": "en", "n_whitespaces": 50, "n_words": 34, "vocab_size": 25 }

{ "docstring": "\n Perform pre-test setup:\n * If the class has an 'available_apps' attribute, restrict the app\n registry to these applications, then fire the post_migrate signal --\n it must run with the correct set of applications for the test case.\n * If the class has a 'fixtures' attribute, install those fixtures.\n ", "language": "en", "n_whitespaces": 95, "n_words": 48, "vocab_size": 38 }

{ "docstring": "\n Actions come from the form whose submit button was pressed (#10618).\n ", "language": "en", "n_whitespaces": 26, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n Sending a message that is empty or only whitespace should fail\n ", "language": "en", "n_whitespaces": 26, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n This optional function will be called for all include_timeframes (including corr_pairs).\n After that, the features will be shifted by the number of candles in the\n include_shifted_candles.\n :param df: strategy dataframe which will receive the features\n dataframe[\"%-pct-change\"] = dataframe[\"close\"].pct_change()\n ", "language": "en", "n_whitespaces": 81, "n_words": 38, "vocab_size": 31 }

{ "docstring": "Gets the next value from the generator `uid`.\n\n To allow multiple generators to be used at the same time, we use `uid` to\n get a specific one. A single generator would cause the validation to\n overwrite the training generator.\n\n Args:\n uid: int, generator identifier\n\n Returns:\n The next value of generator `uid`.\n ", "language": "en", "n_whitespaces": 83, "n_words": 51, "vocab_size": 39 }

{ "docstring": "\n Check if the staticfiles settings have sane values.\n ", "language": "en", "n_whitespaces": 15, "n_words": 8, "vocab_size": 8 }

{ "docstring": "Returns dictionary in Kubernetes format.\n\n Dictionary can be yaml-dumped to a Serve config file directly and then\n copy-pasted into a RayService Kubernetes config.\n\n Args: all kwargs are passed directly into schema's dict() function.\n ", "language": "en", "n_whitespaces": 61, "n_words": 33, "vocab_size": 29 }

{ "docstring": "\n Reloads the image list by replacing the comparative values with those\n that the chosen grouping method expects.\n :param group_method: str name of the grouping method that will be used.\n :param img_list: image list that has been sorted by one of the sort\n methods.\n :return: img_list but with the comparative values that the chosen\n grouping method expects.\n ", "language": "en", "n_whitespaces": 113, "n_words": 56, "vocab_size": 33 }

{ "docstring": "\n\tSELECT so.`name`, so.`base_grand_total`, soi.prevdoc_docname as quotation\n\tFROM `tabSales Order` so, `tabSales Order Item` soi\n\tWHERE so.docstatus=1 AND so.name = soi.parent AND soi.prevdoc_docname in ({0})\n\t", "language": "en", "n_whitespaces": 21, "n_words": 24, "vocab_size": 21 }

{ "docstring": "Download and extract a zip/tar file.\n\n Defined in :numref:`sec_utils`", "language": "en", "n_whitespaces": 11, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Check if the sample weight and the mode match or not.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 10 }

{ "docstring": "Run a cell via a shell command\n\n The `%%script` line is like the #! line of script,\n specifying a program (bash, perl, ruby, etc.) with which to run.\n\n The rest of the cell is run by that program.\n\n Examples\n --------\n ::\n\n In [1]: %%script bash\n ...: for i in 1 2 3; do\n ...: echo $i\n ...: done\n 1\n 2\n 3\n ", "language": "en", "n_whitespaces": 198, "n_words": 61, "vocab_size": 49 }

{ "docstring": "Return a dictionary of runtime statistics.\n\n This dictionary will be empty when the controller has never been\n ran. When it is running or has ran previously it should have (but\n may not) have useful and/or informational keys and values when\n running is underway and/or completed.\n\n .. warning:: The keys in this dictionary **should** be some what\n stable (not changing), but there existence **may**\n change between major releases as new statistics are\n gathered or removed so before accessing keys ensure that\n they actually exist and handle when they do not.\n\n .. note:: The values in this dictionary are local to the thread\n running call (so if multiple threads share the same retrying\n object - either directly or indirectly) they will each have\n there own view of statistics they have collected (in the\n future we may provide a way to aggregate the various\n statistics from each thread).\n ", "language": "en", "n_whitespaces": 359, "n_words": 145, "vocab_size": 103 }

{ "docstring": "\n Send signal from sender to all connected receivers.\n\n If any receiver raises an error, the error propagates back through send,\n terminating the dispatch loop. So it's possible that all receivers\n won't be called if an error is raised.\n\n Arguments:\n\n sender\n The sender of the signal. Either a specific object or None.\n\n named\n Named arguments which will be passed to receivers.\n\n Return a list of tuple pairs [(receiver, response), ... ].\n ", "language": "en", "n_whitespaces": 172, "n_words": 70, "vocab_size": 58 }

{ "docstring": "\n Execute a function on an axis partition in a worker process.\n\n Parameters\n ----------\n func : callable\n Function to be executed on an axis partition.\n *args : iterable\n Additional arguments that need to passed in ``func``.\n\n Returns\n -------\n list\n The result of the function ``func`` and metadata for it.\n\n Notes\n -----\n Ray functions are not detected by codecov (thus pragma: no cover).\n ", "language": "en", "n_whitespaces": 119, "n_words": 61, "vocab_size": 53 }

{ "docstring": "\n This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`ViltConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\n Args:\n input_ids (`torch.LongTensor` of shape `({0})`):\n Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See\n [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input\n IDs?](../glossary#input-ids)\n\n attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n [What are attention masks?](../glossary#attention-mask)\n\n token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):\n Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n 1]`:\n - 0 corresponds to a *sentence A* token,\n - 1 corresponds to a *sentence B* token.\n [What are token type IDs?](../glossary#token-type-ids)\n\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See\n [`ViltFeatureExtractor.__call__`] for details.\n\n pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):\n Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:\n\n - 1 for pixels that are real (i.e. **not masked**),\n - 0 for pixels that are padding (i.e. **masked**).\n `What are attention masks? <../glossary.html#attention-mask>`__\n\n head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n\n inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n model's internal embedding lookup matrix.\n\n image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):\n Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `pixel_values` into patch embeddings.\n\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n\n Args:\n input_ids (`torch.LongTensor` of shape `({0})`):\n Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See\n [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input\n IDs?](../glossary#input-ids)\n\n attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n [What are attention masks?](../glossary#attention-mask)\n\n token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):\n Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n 1]`:\n - 0 corresponds to a *sentence A* token,\n - 1 corresponds to a *sentence B* token.\n [What are token type IDs?](../glossary#token-type-ids)\n\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_images, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See\n [`ViltFeatureExtractor.__call__`] for details.\n\n pixel_mask (`torch.LongTensor` of shape `(batch_size, num_images, height, width)`, *optional*):\n Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:\n\n - 1 for pixels that are real (i.e. **not masked**),\n - 0 for pixels that are padding (i.e. **masked**).\n `What are attention masks? <../glossary.html#attention-mask>`__\n\n head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n\n inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n model's internal embedding lookup matrix.\n\n image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):\n Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `pixel_values` into patch embeddings.\n\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n", "language": "en", "n_whitespaces": 1685, "n_words": 802, "vocab_size": 200 }

{ "docstring": "Check whether to force @run-at document-end.\n\n This needs to be done on QtWebEngine for known-broken scripts.\n\n On Qt 5.12, accessing the DOM isn't possible with \"@run-at\n document-start\". It was documented to be impossible before, but seems\n to work fine.\n\n However, some scripts do DOM access with \"@run-at document-start\". Fix\n those by forcing them to use document-end instead.\n ", "language": "en", "n_whitespaces": 106, "n_words": 57, "vocab_size": 48 }

{ "docstring": "Return the converted unit of measurement for temperature.\n\n Should not be set by integrations.\n ", "language": "en", "n_whitespaces": 28, "n_words": 14, "vocab_size": 14 }

{ "docstring": "\n Select a font based on fontprop and return a name suitable for\n Op.selectfont. If fontprop is a string, it will be interpreted\n as the filename of the font.\n ", "language": "en", "n_whitespaces": 57, "n_words": 28, "vocab_size": 24 }

{ "docstring": "\n Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format\n (left, top, right, bottom).\n ", "language": "en", "n_whitespaces": 31, "n_words": 21, "vocab_size": 19 }

{ "docstring": "\n Set a value in the cache if the key does not already exist. If\n timeout is given, use that timeout for the key; otherwise use the\n default cache timeout.\n\n Return True if the value was stored, False otherwise.\n ", "language": "en", "n_whitespaces": 74, "n_words": 38, "vocab_size": 29 }

{ "docstring": "Returns the normalized Laplacian matrix of G.\n\n The normalized graph Laplacian is the matrix\n\n .. math::\n\n N = D^{-1/2} L D^{-1/2}\n\n where `L` is the graph Laplacian and `D` is the diagonal matrix of\n node degrees [1]_.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph\n\n nodelist : list, optional\n The rows and columns are ordered according to the nodes in nodelist.\n If nodelist is None, then the ordering is produced by G.nodes().\n\n weight : string or None, optional (default='weight')\n The edge data key used to compute each value in the matrix.\n If None, then each edge has weight 1.\n\n Returns\n -------\n N : SciPy sparse array\n The normalized Laplacian matrix of G.\n\n Notes\n -----\n For MultiGraph, the edges weights are summed.\n See :func:`to_numpy_array` for other options.\n\n If the Graph contains selfloops, D is defined as ``diag(sum(A, 1))``, where A is\n the adjacency matrix [2]_.\n\n See Also\n --------\n laplacian_matrix\n normalized_laplacian_spectrum\n\n References\n ----------\n .. [1] Fan Chung-Graham, Spectral Graph Theory,\n CBMS Regional Conference Series in Mathematics, Number 92, 1997.\n .. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized\n Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,\n March 2007.\n ", "language": "en", "n_whitespaces": 331, "n_words": 190, "vocab_size": 126 }

{ "docstring": "Reshape a 2D image into a collection of patches.\n\n The resulting patches are allocated in a dedicated array.\n\n Read more in the :ref:`User Guide <image_feature_extraction>`.\n\n Parameters\n ----------\n image : ndarray of shape (image_height, image_width) or \\\n (image_height, image_width, n_channels)\n The original image data. For color images, the last dimension specifies\n the channel: a RGB image would have `n_channels=3`.\n\n patch_size : tuple of int (patch_height, patch_width)\n The dimensions of one patch.\n\n max_patches : int or float, default=None\n The maximum number of patches to extract. If `max_patches` is a float\n between 0 and 1, it is taken to be a proportion of the total number\n of patches.\n\n random_state : int, RandomState instance, default=None\n Determines the random number generator used for random sampling when\n `max_patches` is not None. Use an int to make the randomness\n deterministic.\n See :term:`Glossary <random_state>`.\n\n Returns\n -------\n patches : array of shape (n_patches, patch_height, patch_width) or \\\n (n_patches, patch_height, patch_width, n_channels)\n The collection of patches extracted from the image, where `n_patches`\n is either `max_patches` or the total number of patches that can be\n extracted.\n\n Examples\n --------\n >>> from sklearn.datasets import load_sample_image\n >>> from sklearn.feature_extraction import image\n >>> # Use the array data from the first image in this dataset:\n >>> one_image = load_sample_image(\"china.jpg\")\n >>> print('Image shape: {}'.format(one_image.shape))\n Image shape: (427, 640, 3)\n >>> patches = image.extract_patches_2d(one_image, (2, 2))\n >>> print('Patches shape: {}'.format(patches.shape))\n Patches shape: (272214, 2, 2, 3)\n >>> # Here are just two of these patches:\n >>> print(patches[1])\n [[[174 201 231]\n [174 201 231]]\n [[173 200 230]\n [173 200 230]]]\n >>> print(patches[800])\n [[[187 214 243]\n [188 215 244]]\n [[187 214 243]\n [188 215 244]]]\n ", "language": "en", "n_whitespaces": 483, "n_words": 266, "vocab_size": 165 }

{ "docstring": "\n Reload field values from the database.\n\n By default, the reloading happens from the database this instance was\n loaded from, or by the read router if this instance wasn't loaded from\n any database. The using parameter will override the default.\n\n Fields can be used to specify which fields to reload. The fields\n should be an iterable of field attnames. If fields is None, then\n all non-deferred fields are reloaded.\n\n When accessing deferred fields of an instance, the deferred loading\n of the field will call this method.\n ", "language": "en", "n_whitespaces": 156, "n_words": 85, "vocab_size": 58 }

{ "docstring": "Set up host and port for HTTP CONNECT tunnelling.\n\n In a connection that uses HTTP CONNECT tunneling, the host passed to the\n constructor is used as a proxy server that relays all communication to\n the endpoint passed to `set_tunnel`. This done by sending an HTTP\n CONNECT request to the proxy server when the connection is established.\n\n This method must be called before the HTTP connection has been\n established.\n\n The headers argument should be a mapping of extra HTTP headers to send\n with the CONNECT request.\n ", "language": "en", "n_whitespaces": 148, "n_words": 85, "vocab_size": 54 }

{ "docstring": "Indicates that the current context is an explicit device_get() call.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Information about the make and model of an accessory.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n Ensure object appears in the mro even\n for old-style classes.\n ", "language": "en", "n_whitespaces": 20, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Early data files, from user options that work with file system.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": "Return the given kwargs as a dictionary with values converted to strings. Items with a value of None will be omitted.", "language": "en", "n_whitespaces": 20, "n_words": 21, "vocab_size": 19 }