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

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{ "docstring": " \n Auto download inference model if the model_path is a url link. \n Otherwise it will use the model_path directly.\n ", "language": "en", "n_whitespaces": 38, "n_words": 18, "vocab_size": 16 }

{ "docstring": "\n returns the specified (x,y) component\n ", "language": "en", "n_whitespaces": 20, "n_words": 5, "vocab_size": 5 }

{ "docstring": "\n Toggle visibility of ticks, ticklabels, and (axis) label.\n To turn all off, ::\n\n axis.toggle(all=False)\n\n To turn all off but ticks on ::\n\n axis.toggle(all=False, ticks=True)\n\n To turn all on but (axis) label off ::\n\n axis.toggle(all=True, label=False)\n\n ", "language": "en", "n_whitespaces": 98, "n_words": 35, "vocab_size": 23 }

{ "docstring": "\n JSON schema with the master version and only mocked nodes.\n ", "language": "en", "n_whitespaces": 17, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Check whether the current scope supports NCHW ops.\n\n TensorFlow does not support NCHW on CPU. Therefore we check if we are not\n explicitly put on\n CPU, and have GPUs available. In this case there will be soft-placing on the\n GPU device.\n\n Returns:\n bool: if the current scope device placement would support nchw\n ", "language": "en", "n_whitespaces": 77, "n_words": 52, "vocab_size": 41 }

{ "docstring": "\n Converts a torch Tensor into an image Numpy array of BGR channel order\n Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order\n Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)\n \n# --------------------------------------------\n# Augmentation, flipe and/or rotate\n# --------------------------------------------\n# The following two are enough.\n# (1) augmet_img: numpy image of WxHxC or WxH\n# (2) augment_img_tensor4: tensor image 1xCxWxH\n# --------------------------------------------\n", "language": "en", "n_whitespaces": 68, "n_words": 62, "vocab_size": 46 }

{ "docstring": "Gets the public IP address or returns the string \"No internet connection\" if unable to obtain it.", "language": "en", "n_whitespaces": 16, "n_words": 17, "vocab_size": 16 }

{ "docstring": "Returns the TipableVMobject instance's (first) tip,\n otherwise throws an exception.", "language": "en", "n_whitespaces": 16, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n Shuffle the order of the data in this axis partition based on the `lengths`.\n\n Parameters\n ----------\n func : callable\n The function to apply before splitting.\n lengths : list\n The list of partition lengths to split the result into.\n **kwargs : dict\n Additional keywords arguments to be passed in `func`.\n\n Returns\n -------\n list\n A list of `PandasDataframePartition` objects split by `lengths`.\n ", "language": "en", "n_whitespaces": 175, "n_words": 60, "vocab_size": 42 }

{ "docstring": "Test that the background update to populate the `room_type` column in\n `room_stats_state` works properly.\n ", "language": "en", "n_whitespaces": 28, "n_words": 14, "vocab_size": 13 }

{ "docstring": "\n This endpoint allows the API user to retrieve all the feedback that has been submitted\n through the `POST /feedback` endpoint.\n ", "language": "en", "n_whitespaces": 30, "n_words": 20, "vocab_size": 18 }

{ "docstring": "Lookup and instantiate a formatter by filename pattern.\n\n Raises ClassNotFound if not found.\n ", "language": "en", "n_whitespaces": 19, "n_words": 13, "vocab_size": 13 }

{ "docstring": "\n Uses prctl to automatically send SIGTERM to the child process when its parent is\n dead. The child process itself should handle SIGTERM properly.\n ", "language": "en", "n_whitespaces": 33, "n_words": 23, "vocab_size": 19 }

{ "docstring": "Force full evaluation of the blocks of this dataset.\n\n This can be used to read all blocks into memory. By default, Datasets\n doesn't read blocks from the datasource until the first transform.\n ", "language": "en", "n_whitespaces": 53, "n_words": 32, "vocab_size": 26 }

{ "docstring": "Iterate map with information regarding each widget and is position\n\n Yields:\n Iterator[tuple[Widget, Region, Region, Size, Size]]: Iterates a tuple of\n Widget, clip region, region, virtual size, and container size.\n ", "language": "en", "n_whitespaces": 69, "n_words": 29, "vocab_size": 26 }

{ "docstring": "\n Returns the field names of the input datatype as a tuple. Input datatype\n has to have fields otherwise error is raised.\n Nested structure are flattened beforehand.\n\n Parameters\n ----------\n adtype : dtype\n Input datatype\n\n Examples\n --------\n >>> from numpy.lib import recfunctions as rfn\n >>> rfn.get_names_flat(np.empty((1,), dtype=[('A', int)]).dtype) is None\n False\n >>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', str)]).dtype)\n ('A', 'B')\n >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])\n >>> rfn.get_names_flat(adtype)\n ('a', 'b', 'ba', 'bb')\n ", "language": "en", "n_whitespaces": 131, "n_words": 72, "vocab_size": 59 }

{ "docstring": "Check that every plot type gets properly collected.", "language": "en", "n_whitespaces": 7, "n_words": 8, "vocab_size": 8 }

{ "docstring": "Run code with the exception hook temporarily disabled.", "language": "en", "n_whitespaces": 7, "n_words": 8, "vocab_size": 8 }

{ "docstring": "\n Trigger import execution for DataFrames obtained by HDK engine.\n\n Parameters\n ----------\n *dfs : iterable\n DataFrames to trigger import.\n ", "language": "en", "n_whitespaces": 41, "n_words": 18, "vocab_size": 17 }

{ "docstring": "Compute the jacobian of `f` at `primals` using forward-mode autodiff.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Records timing information for a server resource.\n\n :param name: The name of the resource.\n :type name: string\n\n :param duration: The time in seconds to report. Internally, this\n is rounded to the nearest millisecond.\n :type duration: float or None\n\n :param description: A description of the resource.\n :type description: string or None\n ", "language": "en", "n_whitespaces": 110, "n_words": 50, "vocab_size": 33 }

{ "docstring": "\n Track the number of times the action field has been rendered on the page,\n so we know which value to use.\n ", "language": "en", "n_whitespaces": 31, "n_words": 21, "vocab_size": 19 }

{ "docstring": "Test if all v2 lights get created with correct features.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n Modify each output message to add in an OPT-compatible context string.\n ", "language": "en", "n_whitespaces": 26, "n_words": 11, "vocab_size": 11 }

{ "docstring": " Obtain the full path to input locations. Will be a list of locations if batch mode is\n selected, or a containing a single location if batch mode is not selected.\n\n Returns\n -------\n list:\n The list of input location paths\n ", "language": "en", "n_whitespaces": 86, "n_words": 39, "vocab_size": 29 }

{ "docstring": "select account_currency from `tabGL Entry`\n\t\t\twhere docstatus=1 and company=%(company)s and party_type=%(party_type)s and party=%(party)s\n\t\t\tlimit 1", "language": "en", "n_whitespaces": 12, "n_words": 15, "vocab_size": 13 }

{ "docstring": "Load and return the breast cancer wisconsin dataset (classification).\n\n The breast cancer dataset is a classic and very easy binary classification\n dataset.\n\n ================= ==============\n Classes 2\n Samples per class 212(M),357(B)\n Samples total 569\n Dimensionality 30\n Features real, positive\n ================= ==============\n\n The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is\n downloaded from:\n https://goo.gl/U2Uwz2\n\n Read more in the :ref:`User Guide <breast_cancer_dataset>`.\n\n Parameters\n ----------\n return_X_y : bool, default=False\n If True, returns ``(data, target)`` instead of a Bunch object.\n See below for more information about the `data` and `target` object.\n\n .. versionadded:: 0.18\n\n as_frame : bool, default=False\n If True, the data is a pandas DataFrame including columns with\n appropriate dtypes (numeric). The target is\n a pandas DataFrame or Series depending on the number of target columns.\n If `return_X_y` is True, then (`data`, `target`) will be pandas\n DataFrames or Series as described below.\n\n .. versionadded:: 0.23\n\n Returns\n -------\n data : :class:`~sklearn.utils.Bunch`\n Dictionary-like object, with the following attributes.\n\n data : {ndarray, dataframe} of shape (569, 30)\n The data matrix. If `as_frame=True`, `data` will be a pandas\n DataFrame.\n target : {ndarray, Series} of shape (569,)\n The classification target. If `as_frame=True`, `target` will be\n a pandas Series.\n feature_names : list\n The names of the dataset columns.\n target_names : list\n The names of target classes.\n frame : DataFrame of shape (569, 31)\n Only present when `as_frame=True`. DataFrame with `data` and\n `target`.\n\n .. versionadded:: 0.23\n DESCR : str\n The full description of the dataset.\n filename : str\n The path to the location of the data.\n\n .. versionadded:: 0.20\n\n (data, target) : tuple if ``return_X_y`` is True\n A tuple of two ndarrays by default. The first contains a 2D ndarray of\n shape (569, 30) with each row representing one sample and each column\n representing the features. The second ndarray of shape (569,) contains\n the target samples. If `as_frame=True`, both arrays are pandas objects,\n i.e. `X` a dataframe and `y` a series.\n\n .. versionadded:: 0.18\n\n Examples\n --------\n Let's say you are interested in the samples 10, 50, and 85, and want to\n know their class name.\n\n >>> from sklearn.datasets import load_breast_cancer\n >>> data = load_breast_cancer()\n >>> data.target[[10, 50, 85]]\n array([0, 1, 0])\n >>> list(data.target_names)\n ['malignant', 'benign']\n ", "language": "en", "n_whitespaces": 823, "n_words": 356, "vocab_size": 205 }

{ "docstring": "Return an instantiated filter.\n\n Options are passed to the filter initializer if wanted.\n Raise a ClassNotFound if not found.\n ", "language": "en", "n_whitespaces": 28, "n_words": 19, "vocab_size": 18 }

{ "docstring": "Wide ResNet-50-2 model from\n `\"Wide Residual Networks\" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n\n Examples:\n .. code-block:: python\n\n import paddle\n from paddle.vision.models import wide_resnet50_2\n\n # build model\n model = wide_resnet50_2()\n\n # build model and load imagenet pretrained weight\n # model = wide_resnet50_2(pretrained=True)\n\n x = paddle.rand([1, 3, 224, 224])\n out = model(x)\n\n print(out.shape)\n ", "language": "en", "n_whitespaces": 182, "n_words": 57, "vocab_size": 43 }

{ "docstring": "\n The endpoint should return a HTTPStatus.OK response if the username does not exist\n ", "language": "en", "n_whitespaces": 28, "n_words": 13, "vocab_size": 13 }

{ "docstring": "Test case 4 - include and exclude specified, both have globs, and a specifically included entity.", "language": "en", "n_whitespaces": 15, "n_words": 16, "vocab_size": 15 }

{ "docstring": "\n\t\tselect distinct so.name, so.transaction_date, so.customer, so.base_grand_total\n\t\tfrom `tabSales Order` so, `tabSales Order Item` so_item\n\t\twhere so_item.parent = so.name\n\t\t\tand so.docstatus = 1 and so.status not in (\"Stopped\", \"Closed\")\n\t\t\tand so.company = %(company)s\n\t\t\tand so_item.qty > so_item.work_order_qty {so_filter} {item_filter}\n\t\t\tand (exists (select name from `tabBOM` bom where {bom_item}\n\t\t\t\t\tand bom.is_active = 1)\n\t\t\t\tor exists (select name from `tabPacked Item` pi\n\t\t\t\t\twhere pi.parent = so.name and pi.parent_item = so_item.item_code\n\t\t\t\t\t\tand exists (select name from `tabBOM` bom where bom.item=pi.item_code\n\t\t\t\t\t\t\tand bom.is_active = 1)))\n\t\t", "language": "en", "n_whitespaces": 68, "n_words": 80, "vocab_size": 49 }

{ "docstring": "Test that copying of a published page emits a page_published signal.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 10 }

{ "docstring": "\n Return a list of paths to all test folders and files under `tests`. All paths are rooted at `tests`.\n\n - folders under `tests`: `tokenization`, `pipelines`, etc. The folder `models` is excluded.\n - folders under `tests/models`: `bert`, `gpt2`, etc.\n - test files under `tests`: `test_modeling_common.py`, `test_tokenization_common.py`, etc.\n ", "language": "en", "n_whitespaces": 62, "n_words": 46, "vocab_size": 32 }

{ "docstring": "Tests V-trace against ground truth data calculated in python.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n pdfdump(filename=None, layer_shift=0, rebuild=1)\n\n Creates a PDF file describing a packet. If filename is not provided a\n temporary file is created and xpdf is called.\n\n :param filename: the file's filename\n ", "language": "en", "n_whitespaces": 65, "n_words": 29, "vocab_size": 23 }

{ "docstring": "\n Select input value according to index\n \n Arags：\n input: input matrix\n index: index matrix\n Returns:\n output\n >>> input\n [\n [1, 2, 3],\n [4, 5, 6]\n ]\n >>> index\n [\n [1, 2],\n [0, 1]\n ]\n >>> index_sample(input, index)\n [\n [2, 3],\n [4, 5]\n ]\n ", "language": "en", "n_whitespaces": 149, "n_words": 42, "vocab_size": 28 }

{ "docstring": "\nThe 'names' argument to CoordSystem is deprecated. Use 'symbols' instead. That\nis, replace\n\n CoordSystem(..., names={names})\n\nwith\n\n CoordSystem(..., symbols=[{', '.join([\"Symbol(\" + repr(n) + \", real=True)\" for n in names])}])\n \n\nPassing a string as the coordinate symbol name to CoordSystem is deprecated.\nPass a Symbol with the appropriate name and assumptions instead.\n\nThat is, replace {s} with Symbol({s!r}, real=True).\n \n CoordSystem.transforms is deprecated. The CoordSystem class is now\n immutable. Use the 'relations' keyword argument to the\n CoordSystems() constructor to specify relations.\n ", "language": "en", "n_whitespaces": 167, "n_words": 78, "vocab_size": 52 }

{ "docstring": "A model to complete on open tabs in the current window.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n\t\t\tselect t.owner,\n\t\t\t t.posting_date,\n\t\t\t\t t.mode_of_payment,\n\t\t\t\t sum(t.paid_amount) as paid_amount\n\t\t\tfrom (\n\t\t\t\tselect a.owner, a.posting_date,\n\t\t\t\tifnull(b.mode_of_payment, '') as mode_of_payment, sum(b.base_amount) as paid_amount\n\t\t\t\tfrom `tabSales Invoice` a, `tabSales Invoice Payment` b\n\t\t\t\twhere a.name = b.parent\n\t\t\t\tand a.docstatus = 1\n\t\t\t\tand a.name in ({invoice_list_names})\n\t\t\t\tgroup by a.owner, a.posting_date, mode_of_payment\n\t\t\t\tunion\n\t\t\t\tselect a.owner,a.posting_date,\n\t\t\t\tifnull(b.mode_of_payment, '') as mode_of_payment, sum(c.allocated_amount) as paid_amount\n\t\t\t\tfrom `tabSales Invoice` a, `tabPayment Entry` b,`tabPayment Entry Reference` c\n\t\t\t\twhere a.name = c.reference_name\n\t\t\t\tand b.name = c.parent\n\t\t\t\tand b.docstatus = 1\n\t\t\t\tand a.name in ({invoice_list_names})\n\t\t\t\tgroup by a.owner, a.posting_date, mode_of_payment\n\t\t\t\tunion\n\t\t\t\tselect a.owner, a.posting_date,\n\t\t\t\tifnull(a.voucher_type,'') as mode_of_payment, sum(b.credit)\n\t\t\t\tfrom `tabJournal Entry` a, `tabJournal Entry Account` b\n\t\t\t\twhere a.name = b.parent\n\t\t\t\tand a.docstatus = 1\n\t\t\t\tand b.reference_type = 'Sales Invoice'\n\t\t\t\tand b.reference_name in ({invoice_list_names})\n\t\t\t\tgroup by a.owner, a.posting_date, mode_of_payment\n\t\t\t) t\n\t\t\tgroup by t.owner, t.posting_date, t.mode_of_payment\n\t\t\tselect a.owner, a.posting_date,\n\t\t\tifnull(b.mode_of_payment, '') as mode_of_payment, sum(a.base_change_amount) as change_amount\n\t\t\tfrom `tabSales Invoice` a, `tabSales Invoice Payment` b\n\t\t\twhere a.name = b.parent\n\t\t\tand a.name in ({invoice_list_names})\n\t\t\tand b.type = 'Cash'\n\t\t\tand a.base_change_amount > 0\n\t\t\tgroup by a.owner, a.posting_date, mode_of_payment", "language": "en", "n_whitespaces": 142, "n_words": 169, "vocab_size": 64 }

{ "docstring": "\n Compile all config data, overwriting lower-weight values with higher-weight values where a collision occurs.\n Return the rendered configuration context for a device or VM.\n ", "language": "en", "n_whitespaces": 46, "n_words": 24, "vocab_size": 22 }

{ "docstring": "\n Tests that sending a server notices is successfully,\n if a display_name is set, avatar_url is `None` and\n \"check avatar size and mime type\" is set.\n ", "language": "en", "n_whitespaces": 54, "n_words": 25, "vocab_size": 20 }

{ "docstring": "Checks if all iterations have completed.\n\n TODO(rliaw): also check that `t.iterations == self._r`", "language": "en", "n_whitespaces": 19, "n_words": 13, "vocab_size": 13 }

{ "docstring": "select distinct YEAR(date) from `tabStudent Attendance` ORDER BY YEAR(date) DESC", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 9 }

{ "docstring": "Test that we create/dismiss a notification when source is discovery.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Produce a sort key for given requirement based on preference.\n\n The preference is defined as \"I think this requirement should be\n resolved first\". The lower the return value is, the more preferred\n this group of arguments is.\n\n :param identifier: An identifier as returned by ``identify()``. This\n identifies the dependency matches of which should be returned.\n :param resolutions: Mapping of candidates currently pinned by the\n resolver. Each key is an identifier, and the value a candidate.\n The candidate may conflict with requirements from ``information``.\n :param candidates: Mapping of each dependency's possible candidates.\n Each value is an iterator of candidates.\n :param information: Mapping of requirement information of each package.\n Each value is an iterator of *requirement information*.\n\n A *requirement information* instance is a named tuple with two members:\n\n * ``requirement`` specifies a requirement contributing to the current\n list of candidates.\n * ``parent`` specifies the candidate that provides (dependend on) the\n requirement, or ``None`` to indicate a root requirement.\n\n The preference could depend on a various of issues, including (not\n necessarily in this order):\n\n * Is this package pinned in the current resolution result?\n * How relaxed is the requirement? Stricter ones should probably be\n worked on first? (I don't know, actually.)\n * How many possibilities are there to satisfy this requirement? Those\n with few left should likely be worked on first, I guess?\n * Are there any known conflicts for this requirement? We should\n probably work on those with the most known conflicts.\n\n A sortable value should be returned (this will be used as the ``key``\n parameter of the built-in sorting function). The smaller the value is,\n the more preferred this requirement is (i.e. the sorting function\n is called with ``reverse=False``).\n ", "language": "en", "n_whitespaces": 526, "n_words": 279, "vocab_size": 160 }

{ "docstring": "Ensure that project metadata is available.\n\n Under PEP 517 and PEP 660, call the backend hook to prepare the metadata.\n Under legacy processing, call setup.py egg-info.\n ", "language": "en", "n_whitespaces": 47, "n_words": 26, "vocab_size": 22 }

{ "docstring": "\n Get the generated filename for a resized image\n ", "language": "en", "n_whitespaces": 23, "n_words": 8, "vocab_size": 8 }

{ "docstring": "Returns the smaller value.\n\n Like min(self, other) except if one is not a number, returns\n NaN (and signals if one is sNaN). Also rounds.\n ", "language": "en", "n_whitespaces": 46, "n_words": 24, "vocab_size": 21 }

{ "docstring": "\n A SimpleListFilter lookups method can return None but disables the\n filter completely.\n ", "language": "en", "n_whitespaces": 34, "n_words": 12, "vocab_size": 12 }

{ "docstring": "Precomputes the fill value for a feature.\n\n NOTE: this is called before NaNs are removed from the dataset. Modifications here must handle NaNs gracefully.\n ", "language": "en", "n_whitespaces": 30, "n_words": 24, "vocab_size": 22 }

{ "docstring": "Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n ", "language": "en", "n_whitespaces": 30, "n_words": 16, "vocab_size": 16 }

{ "docstring": "Check the behaviour for the multilabel classification case and the\n `predict_proba` stacking method.\n\n Estimators are not consistent with the output arrays and we need to ensure that\n we handle all cases.\n ", "language": "en", "n_whitespaces": 43, "n_words": 31, "vocab_size": 26 }

{ "docstring": "\n Get the HTML for an URL, possibly from an in-memory cache.\n\n XXX TODO Note: this cache is never actually cleared. It's assumed that\n the data won't get stale over the lifetime of a locator instance (not\n necessarily true for the default_locator).\n ", "language": "en", "n_whitespaces": 77, "n_words": 41, "vocab_size": 36 }

{ "docstring": "\n Enable all global pyparsing diagnostic warnings (see :class:`Diagnostics`).\n ", "language": "en", "n_whitespaces": 15, "n_words": 8, "vocab_size": 8 }

{ "docstring": "\n Export the queryset of objects as concatenated YAML documents.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n Return ``True`` if the url uses an allowed host and a safe scheme.\n\n Always return ``False`` on an empty url.\n\n If ``require_https`` is ``True``, only 'https' will be considered a valid\n scheme, as opposed to 'http' and 'https' with the default, ``False``.\n\n Note: \"True\" doesn't entail that a URL is \"safe\". It may still be e.g.\n quoted incorrectly. Ensure to also use django.utils.encoding.iri_to_uri()\n on the path component of untrusted URLs.\n ", "language": "en", "n_whitespaces": 95, "n_words": 70, "vocab_size": 59 }

{ "docstring": "\n Last resort to find a service by trying to match pattern to programs in memory\n ", "language": "en", "n_whitespaces": 22, "n_words": 15, "vocab_size": 13 }

{ "docstring": "Normalize the image.\n Operators:\n 1.(optional) Scale the pixel to [0,1]\n 2.(optional) Each pixel minus mean and is divided by std\n ", "language": "en", "n_whitespaces": 56, "n_words": 20, "vocab_size": 18 }

{ "docstring": "Check if no relationship model is used by more than one m2m field.", "language": "en", "n_whitespaces": 12, "n_words": 13, "vocab_size": 13 }

{ "docstring": " :class:`numpy.ndarray`: The source frame for this object. ", "language": "en", "n_whitespaces": 8, "n_words": 7, "vocab_size": 7 }

{ "docstring": "\n Test that an email AND Slack notification are sent with\n the expected values when a release is deployed.\n ", "language": "en", "n_whitespaces": 40, "n_words": 18, "vocab_size": 18 }

{ "docstring": "\n\t\tselect mpa.default_account, mpa.parent, mp.type as type\n\t\tfrom `tabMode of Payment Account` mpa,`tabMode of Payment` mp\n\t\twhere mpa.parent = mp.name and mpa.company = %(company)s and mp.enabled = 1", "language": "en", "n_whitespaces": 24, "n_words": 27, "vocab_size": 23 }

{ "docstring": "\n Mouse moving.\n\n By default, button-1 rotates, button-2 pans, and button-3 zooms;\n these buttons can be modified via `mouse_init`.\n ", "language": "en", "n_whitespaces": 47, "n_words": 18, "vocab_size": 18 }

{ "docstring": "\n Install the named fileset(s)/rpm package(s).\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 Note: use of rpm to install implies that rpm's dependencies must have been previously installed.\n dnf and yum automatically install rpm's dependencies as part of the install process\n\n name\n The name of the fileset or rpm package to be installed.\n\n refresh\n Whether or not to update the yum database before executing.\n\n\n Multiple Package Installation Options:\n\n pkgs\n A list of filesets and/or rpm packages to install.\n Must be passed as a python list. The ``name`` parameter will be\n ignored if this option is passed.\n\n version\n Install a specific version of a fileset/rpm package.\n (Unused at present).\n\n test\n Verify that command functions correctly:\n\n Returns a dict containing the new fileset(s)/rpm package(s) names and versions:\n\n {'<package>': {'old': '<old-version>',\n 'new': '<new-version>'}}\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' pkg.install /stage/middleware/AIX/bash-4.2-3.aix6.1.ppc.rpm\n salt '*' pkg.install /stage/middleware/AIX/bash-4.2-3.aix6.1.ppc.rpm refresh=True\n salt '*' pkg.install /stage/middleware/AIX/VIOS2211_update/tpc_4.1.1.85.bff\n salt '*' pkg.install /stage/middleware/AIX/Xlc/usr/sys/inst.images/xlC.rte\n salt '*' pkg.install /stage/middleware/AIX/Firefox/ppc-AIX53/Firefox.base\n salt '*' pkg.install pkgs='[\"foo\", \"bar\"]'\n ", "language": "en", "n_whitespaces": 405, "n_words": 172, "vocab_size": 115 }

{ "docstring": "\n Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very\n specific question of whether the value is set to `False` (and it's not set to `True`` or isn't set).\n ", "language": "en", "n_whitespaces": 60, "n_words": 38, "vocab_size": 30 }

{ "docstring": "Extracts and saves the parsed structure of all pytorch classes referenced in\n `ludwig.modules.optimization_modules.optimizer_registry` as JSON files under\n `ludwig/validation/generated/torch/`.", "language": "en", "n_whitespaces": 23, "n_words": 18, "vocab_size": 18 }

{ "docstring": "\n CREATE PREDICTOR huggingface.spam_classifier\n predict PRED\n USING\n task='text-classification',\n model_name= \"mrm8488/bert-tiny-finetuned-sms-spam-detection\",\n input_column = 'text_spammy',\n labels=['ham','spam']\n \n SELECT h.*\n FROM pg.df as t \n JOIN huggingface.spam_classifier as h\n ", "language": "en", "n_whitespaces": 166, "n_words": 23, "vocab_size": 21 }

{ "docstring": "\n Make the margins that are submerged inside an Axes the same size.\n\n This allows axes that span two columns (or rows) that are offset\n from one another to have the same size.\n\n This gives the proper layout for something like::\n fig = plt.figure(constrained_layout=True)\n axs = fig.subplot_mosaic(\"AAAB\\nCCDD\")\n\n Without this routine, the axes D will be wider than C, because the\n margin width between the two columns in C has no width by default,\n whereas the margins between the two columns of D are set by the\n width of the margin between A and B. However, obviously the user would\n like C and D to be the same size, so we need to add constraints to these\n \"submerged\" margins.\n\n This routine makes all the interior margins the same, and the spacing\n between the three columns in A and the two column in C are all set to the\n margins between the two columns of D.\n\n See test_constrained_layout::test_constrained_layout12 for an example.\n ", "language": "en", "n_whitespaces": 218, "n_words": 158, "vocab_size": 87 }

{ "docstring": "Parse a version string into a tuple.\n\n Args:\n version_str (str): The version string.\n Returns:\n tuple[int | str]: The version info, e.g., \"1.3.0\" is parsed into\n (1, 3, 0), and \"2.0.0rc1\" is parsed into (2, 0, 0, 'rc1').\n ", "language": "en", "n_whitespaces": 71, "n_words": 37, "vocab_size": 28 }

{ "docstring": "\n Adjusts the application settings to accept calls from a Relay instance running inside a\n docker container.\n\n :param settings: the app settings\n ", "language": "en", "n_whitespaces": 34, "n_words": 21, "vocab_size": 18 }

{ "docstring": "\n Clear all backend clients so that new backend clients can be created later.\n ", "language": "en", "n_whitespaces": 16, "n_words": 13, "vocab_size": 11 }

{ "docstring": "\n Determine the utilization rate of power in the rack and return it as a percentage.\n ", "language": "en", "n_whitespaces": 30, "n_words": 15, "vocab_size": 14 }

{ "docstring": "\n Perform some internal account and cleanup. This is run on\n every cluster node heartbeat:\n\n 1. Discover worker processes that exited, and recover messages they\n were handling.\n 2. Clean up unnecessary, idle workers.\n\n IMPORTANT: this function is one of the few places in the dispatcher\n (aside from setting lookups) where we talk to the database. As such,\n if there's an outage, this method _can_ throw various\n django.db.utils.Error exceptions. Act accordingly.\n ", "language": "en", "n_whitespaces": 149, "n_words": 69, "vocab_size": 64 }

{ "docstring": "\n A new token is sent if the csrf_cookie is the empty string.\n ", "language": "en", "n_whitespaces": 27, "n_words": 12, "vocab_size": 10 }

{ "docstring": "\n This function allows the user to save envs that are alive on the\n Tornado server. The envs can be specified as a list of env ids.\n ", "language": "en", "n_whitespaces": 48, "n_words": 26, "vocab_size": 24 }

{ "docstring": "Joins shouldn't be performed for <O2O>_id fields in list display.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Binary crossentropy between an output tensor and a target tensor.\n\n Args:\n target: A tensor with the same shape as `output`.\n output: A tensor.\n from_logits: Whether `output` is expected to be a logits tensor.\n By default, we consider that `output`\n encodes a probability distribution.\n\n Returns:\n A tensor.\n ", "language": "en", "n_whitespaces": 105, "n_words": 46, "vocab_size": 37 }

{ "docstring": "Ensure that we correctly handle pulled events with lots of missing state\n\n In this test, we pretend we are processing a \"pulled\" event (eg, via backfill\n or get_missing_events). The pulled event has a prev_event we haven't previously\n seen, so the server requests the state at that prev_event. There is a lot\n of state we don't have, so we expect the server to make a /state request.\n\n We check that the pulled event is correctly persisted, and that the state is\n as we expect.\n ", "language": "en", "n_whitespaces": 132, "n_words": 83, "vocab_size": 54 }

{ "docstring": "\n Test that a Slack message is sent with the expected payload when an issue is resolved in a release\n ", "language": "en", "n_whitespaces": 34, "n_words": 19, "vocab_size": 17 }

{ "docstring": "Returns the current vocabulary of the layer.\n\n Args:\n include_special_tokens: If True, the returned vocabulary will include\n mask and OOV tokens, and a term's index in the vocabulary will equal\n the term's index when calling the layer. If False, the returned\n vocabulary will not include any mask or OOV tokens.\n ", "language": "en", "n_whitespaces": 105, "n_words": 49, "vocab_size": 29 }

{ "docstring": "Test that an error is raised if there are not enough batches when passed with float value of\n limit_eval_batches.", "language": "en", "n_whitespaces": 21, "n_words": 19, "vocab_size": 19 }

{ "docstring": "This is to support the backend and device argument on jit. It's a feature\n that's deprecated but needs to be supported for feature parity and so that we\n can delete the non-Array paths when Array is switched on.\n ", "language": "en", "n_whitespaces": 41, "n_words": 38, "vocab_size": 33 }

{ "docstring": "Returns True if the touch event is a mousewheel scrolling\n\n .. versionadded:: 1.6.0\n ", "language": "en", "n_whitespaces": 27, "n_words": 13, "vocab_size": 13 }

{ "docstring": "\n Works like a \"print\" statement but with windowing options. Routes output to the \"Debug Window\"\n\n In addition to the normal text and background colors, you can use a \"colors\" tuple/string\n The \"colors\" or \"c\" parameter defines both the text and background in a single parm.\n It can be a tuple or a single single. Both text and background colors need to be specified\n colors -(str, str) or str. A combined text/background color definition in a single parameter\n c - (str, str) - Colors tuple has format (foreground, backgrouned)\n c - str - can also be a string of the format \"foreground on background\" (\"white on red\")\n\n :param *args: stuff to output\n :type *args: (Any)\n :param size: (w,h) w=characters-wide, h=rows-high\n :type size: (int, int)\n :param end: end character\n :type end: (str)\n :param sep: separator character\n :type sep: (str)\n :param location: Location of upper left corner of the window\n :type location: (int, int)\n :param relative_location: (x,y) location relative to the default location of the window, in pixels. Normally the window centers. This location is relative to the location the window would be created. Note they can be negative.\n :type relative_location: (int, int)\n :param font: specifies the font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike\n :type font: (str or (str, int[, str]) or None)\n :param no_titlebar: If True no titlebar will be shown\n :type no_titlebar: (bool)\n :param no_button: don't show button\n :type no_button: (bool)\n :param grab_anywhere: If True: can grab anywhere to move the window (Default = False)\n :type grab_anywhere: (bool)\n :param background_color: color of background\n :type background_color: (str)\n :param text_color: color of the text\n :type text_color: (str)\n :param keep_on_top: If True the window will remain above all current windows\n :type keep_on_top: (bool)\n :param location: Location of upper left corner of the window\n :type location: (int, int)\n :param do_not_reroute_stdout: do not reroute stdout and stderr. If False, both stdout and stderr will reroute to here\n :type do_not_reroute_stdout: (bool)\n :param echo_stdout: If True stdout is sent to both the console and the debug window\n :type echo_stdout: (bool)\n :param colors: Either a tuple or a string that has both the text and background colors\n :type colors: (str) or (str, str)\n :param c: Either a tuple or a string that has both the text and background colors\n :type c: (str) or (str, str)\n :param resizable: if True, the user can resize the debug window. Default is True\n :type resizable: (bool)\n :param erase_all: If True when erase the output before printing\n :type erase_all: (bool)\n :param blocking: if True, makes the window block instead of returning immediately. The \"Quit\" button changers to \"More\"\n :type blocking: (bool | None)\n :return:\n :rtype:\n ", "language": "en", "n_whitespaces": 1135, "n_words": 444, "vocab_size": 200 }

{ "docstring": "Block until all pods starts successfully.\n\n If not success, it will raise an error hoping the outer function to catch it\n ", "language": "en", "n_whitespaces": 35, "n_words": 21, "vocab_size": 20 }

{ "docstring": "Returns a pair of matrices (`C`, `F`) with matching rank\n such that `A = C F`.\n\n Parameters\n ==========\n\n iszerofunc : Function, optional\n A function used for detecting whether an element can\n act as a pivot. ``lambda x: x.is_zero`` is used by default.\n\n simplify : Bool or Function, optional\n A function used to simplify elements when looking for a\n pivot. By default SymPy's ``simplify`` is used.\n\n Returns\n =======\n\n (C, F) : Matrices\n `C` and `F` are full-rank matrices with rank as same as `A`,\n whose product gives `A`.\n\n See Notes for additional mathematical details.\n\n Examples\n ========\n\n >>> from sympy import Matrix\n >>> A = Matrix([\n ... [1, 3, 1, 4],\n ... [2, 7, 3, 9],\n ... [1, 5, 3, 1],\n ... [1, 2, 0, 8]\n ... ])\n >>> C, F = A.rank_decomposition()\n >>> C\n Matrix([\n [1, 3, 4],\n [2, 7, 9],\n [1, 5, 1],\n [1, 2, 8]])\n >>> F\n Matrix([\n [1, 0, -2, 0],\n [0, 1, 1, 0],\n [0, 0, 0, 1]])\n >>> C * F == A\n True\n\n Notes\n =====\n\n Obtaining `F`, an RREF of `A`, is equivalent to creating a\n product\n\n .. math::\n E_n E_{n-1} ... E_1 A = F\n\n where `E_n, E_{n-1}, \\dots, E_1` are the elimination matrices or\n permutation matrices equivalent to each row-reduction step.\n\n The inverse of the same product of elimination matrices gives\n `C`:\n\n .. math::\n C = \\left(E_n E_{n-1} \\dots E_1\\right)^{-1}\n\n It is not necessary, however, to actually compute the inverse:\n the columns of `C` are those from the original matrix with the\n same column indices as the indices of the pivot columns of `F`.\n\n References\n ==========\n\n .. [1] https://en.wikipedia.org/wiki/Rank_factorization\n\n .. [2] Piziak, R.; Odell, P. L. (1 June 1999).\n \"Full Rank Factorization of Matrices\".\n Mathematics Magazine. 72 (3): 193. doi:10.2307/2690882\n\n See Also\n ========\n\n sympy.matrices.matrices.MatrixReductions.rref\n ", "language": "en", "n_whitespaces": 543, "n_words": 291, "vocab_size": 172 }

{ "docstring": "Scale input vectors individually to unit norm (vector length).\n\n Read more in the :ref:`User Guide <preprocessing_normalization>`.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The data to normalize, element by element.\n scipy.sparse matrices should be in CSR format to avoid an\n un-necessary copy.\n\n norm : {'l1', 'l2', 'max'}, default='l2'\n The norm to use to normalize each non zero sample (or each non-zero\n feature if axis is 0).\n\n axis : {0, 1}, default=1\n axis used to normalize the data along. If 1, independently normalize\n each sample, otherwise (if 0) normalize each feature.\n\n copy : bool, default=True\n Set to False to perform inplace row normalization and avoid a\n copy (if the input is already a numpy array or a scipy.sparse\n CSR matrix and if axis is 1).\n\n return_norm : bool, default=False\n Whether to return the computed norms.\n\n Returns\n -------\n X : {ndarray, sparse matrix} of shape (n_samples, n_features)\n Normalized input X.\n\n norms : ndarray of shape (n_samples, ) if axis=1 else (n_features, )\n An array of norms along given axis for X.\n When X is sparse, a NotImplementedError will be raised\n for norm 'l1' or 'l2'.\n\n See Also\n --------\n Normalizer : Performs normalization using the Transformer API\n (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`).\n\n Notes\n -----\n For a comparison of the different scalers, transformers, and normalizers,\n see :ref:`examples/preprocessing/plot_all_scaling.py\n <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.\n ", "language": "en", "n_whitespaces": 395, "n_words": 220, "vocab_size": 142 }

{ "docstring": "Find a version in the installer from the version supplied.\n\n A ValueError is raised if a matching version cannot be found.\n ", "language": "en", "n_whitespaces": 35, "n_words": 21, "vocab_size": 17 }

{ "docstring": "\n Copy selected attributes from a request object into a new fake request object. This is needed in places where\n thread safe pickling of the useful request data is needed.\n ", "language": "en", "n_whitespaces": 39, "n_words": 29, "vocab_size": 25 }

{ "docstring": "\n This method calls the parent class's batchify method and then add\n classifier_label and is_ltr property to the the batch.\n ", "language": "en", "n_whitespaces": 41, "n_words": 19, "vocab_size": 15 }

{ "docstring": "Height of spec after n convolutions with fixed kernel/stride/pad.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Test rendering the hls playlist with 1 and 2 output segments.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": "Create mapping from worker world_rank to local_rank.\n\n Example:\n Worker 0: 0.0.0.0\n Worker 1: 0.0.0.0\n Worker 2: 0.0.0.1\n Worker 3: 0.0.0.0\n Worker 4: 0.0.0.1\n\n Workers 0, 1, 3 are on 0.0.0.0.\n Workers 2, 4 are on 0.0.0.1.\n\n Expected Output:\n {\n 0 -> 0,\n 1 -> 1,\n 2 -> 0,\n 3 -> 2,\n 4 -> 1\n }\n ", "language": "en", "n_whitespaces": 254, "n_words": 55, "vocab_size": 34 }