Datasets:

semeru

/

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

like 0

Follow

Semeru Lab 7

{ "docstring": "Return a string representing the CLI command associated with the test failure.", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 11 }

{ "docstring": "\n Search for overlap with the observation label.\n\n Return the best fitting document. A document is valid if the f1 is above the\n threshold AND the f1 is less than 1.0 AND the target label is not in the\n document.\n ", "language": "en", "n_whitespaces": 75, "n_words": 39, "vocab_size": 27 }

{ "docstring": "Print help[cmds]\n pick pick target column for analysis[/cmds]\n\n[param]Pair: [/param]{self.ticker}\n[param]Target Column: [/param]{self.target}\n[cmds]\n[info]Statistics:[/info]\n summary brief summary statistics of loaded pair.\n normality normality statistics and tests\n unitroot unit root test for stationarity (ADF, KPSS)\n[info]Plots:[/info]\n line line plot of selected target\n hist histogram with density plot\n cdf cumulative distribution function\n bw box and whisker plot\n acf (partial) auto-correlation function differentials of prices\n qqplot residuals against standard normal curve\n[info]Rolling Metrics:[/info]\n rolling rolling mean and std deviation of prices\n spread rolling variance and std deviation of prices\n quantile rolling median and quantile of prices\n skew rolling skewness of distribution of prices\n kurtosis rolling kurtosis of distribution of prices\n[info]Risk:[/info]\n var display value at risk\n es display expected shortfall\n[info]Other:[/info]\n raw print raw data\n decompose decomposition in cyclic-trend, season, and residuals of prices\n cusum detects abrupt changes using cumulative sum algorithm of prices[/cmds]\n ", "language": "en", "n_whitespaces": 315, "n_words": 142, "vocab_size": 95 }

{ "docstring": "\n Return a copy of the current Query that's ready for another operation.\n The klass argument changes the type of the Query, e.g. UpdateQuery.\n ", "language": "en", "n_whitespaces": 45, "n_words": 23, "vocab_size": 20 }

{ "docstring": "Test that the GBM model can train and predict with non-number inputs.", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 12 }

{ "docstring": "get the length of the field, including the padding length", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 7 }

{ "docstring": "\n Return the amount of kerning between this and the given character.\n\n This method is called when characters are strung together into `Hlist`\n to create `Kern` nodes.\n ", "language": "en", "n_whitespaces": 55, "n_words": 26, "vocab_size": 25 }

{ "docstring": "Returns number of samples or steps, and whether to use steps count mode.", "language": "en", "n_whitespaces": 12, "n_words": 13, "vocab_size": 13 }

{ "docstring": "\n query ($channel: String){\n products(first: 10, channel: $channel) {\n edges {\n node {\n attributes {\n values {\n inputType\n }\n }\n }\n }\n }\n }\n \n mutation ProductTypeReorderAttributes(\n $productTypeId: ID!\n $moves: [ReorderInput!]!\n $type: ProductAttributeType!\n ) {\n productTypeReorderAttributes(\n productTypeId: $productTypeId\n moves: $moves\n type: $type\n ) {\n productType {\n id\n variantAttributes {\n id\n slug\n }\n productAttributes {\n id\n }\n }\n\n errors {\n field\n message\n code\n attributes\n }\n }\n }\n", "language": "en", "n_whitespaces": 433, "n_words": 64, "vocab_size": 39 }

{ "docstring": "\n Take a project_state representing all migrations prior to this one\n and a schema_editor for a live database and apply the migration\n in a forwards order.\n\n Return the resulting project state for efficient reuse by following\n Migrations.\n ", "language": "en", "n_whitespaces": 79, "n_words": 36, "vocab_size": 30 }

{ "docstring": "Returns False iff non_terminated_nodes contains any pods in the RayCluster's\n workersToDelete lists.\n\n Explanation:\n If there are any workersToDelete which are non-terminated,\n we should wait for the operator to do its job and delete those\n pods. Therefore, we back off the autoscaler update.\n\n If, on the other hand, all of the workersToDelete have already been cleaned up,\n then we patch away the workersToDelete lists and return True.\n In the future, we may consider having the operator clean up workersToDelete\n on it own:\n https://github.com/ray-project/kuberay/issues/733\n\n Note (Dmitri):\n It is stylistically bad that this function has a side effect.\n ", "language": "en", "n_whitespaces": 186, "n_words": 95, "vocab_size": 76 }

{ "docstring": "\n Return the distance parameters given the value and the lookup type.\n On Oracle, geometry columns with a geodetic coordinate system behave\n implicitly like a geography column, and thus meters will be used as\n the distance parameter on them.\n ", "language": "en", "n_whitespaces": 74, "n_words": 38, "vocab_size": 32 }

{ "docstring": "Test get_document_model with no WAGTAILDOCS_DOCUMENT_MODEL", "language": "en", "n_whitespaces": 4, "n_words": 5, "vocab_size": 5 }

{ "docstring": "Safe assignment to a numpy array, sparse matrix, or pandas dataframe.\n\n Parameters\n ----------\n X : {ndarray, sparse-matrix, dataframe}\n Array to be modified. It is expected to be 2-dimensional.\n\n values : ndarray\n The values to be assigned to `X`.\n\n row_indexer : array-like, dtype={int, bool}, default=None\n A 1-dimensional array to select the rows of interest. If `None`, all\n rows are selected.\n\n column_indexer : array-like, dtype={int, bool}, default=None\n A 1-dimensional array to select the columns of interest. If `None`, all\n columns are selected.\n ", "language": "en", "n_whitespaces": 143, "n_words": 80, "vocab_size": 50 }

{ "docstring": "Copies the second operand's sign to the first one.\n\n In detail, it returns a copy of the first operand with the sign\n equal to the sign of the second operand.\n\n >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('7.33'))\n Decimal('1.50')\n >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('7.33'))\n Decimal('1.50')\n >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('-7.33'))\n Decimal('-1.50')\n >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('-7.33'))\n Decimal('-1.50')\n >>> ExtendedContext.copy_sign(1, -2)\n Decimal('-1')\n >>> ExtendedContext.copy_sign(Decimal(1), -2)\n Decimal('-1')\n >>> ExtendedContext.copy_sign(1, Decimal(-2))\n Decimal('-1')\n ", "language": "en", "n_whitespaces": 179, "n_words": 60, "vocab_size": 32 }

{ "docstring": " Return a dict of field names with ordering labels if ordering is valid ", "language": "en", "n_whitespaces": 14, "n_words": 13, "vocab_size": 12 }

{ "docstring": "Test for the :func:`networkx.chordal_cycle_graph` function.", "language": "en", "n_whitespaces": 4, "n_words": 5, "vocab_size": 5 }

{ "docstring": "Helper function to convert left_size/right_size relative to dataset's size\n ", "language": "en", "n_whitespaces": 11, "n_words": 9, "vocab_size": 8 }

{ "docstring": "Return count of active, unique hosts for licensing.\n Construction of query involves:\n - remove any ordering specified in model's Meta\n - Exclude hosts sourced from another Tower\n - Restrict the query to only return the name column\n - Only consider results that are unique\n - Return the count of this query\n ", "language": "en", "n_whitespaces": 105, "n_words": 51, "vocab_size": 37 }

{ "docstring": "Tests that saving an avatar fails when its mime type is not allowed", "language": "en", "n_whitespaces": 12, "n_words": 13, "vocab_size": 13 }

{ "docstring": "Returns whether this layer or any of its children uses the training arg.", "language": "en", "n_whitespaces": 12, "n_words": 13, "vocab_size": 13 }

{ "docstring": "\n WITH\n U := (\n SELECT User {\n select_deck := (\n FOR letter IN {'I', 'B'}\n UNION (\n SELECT User.deck {\n name,\n # just define an ad-hoc link prop", "language": "en", "n_whitespaces": 287, "n_words": 28, "vocab_size": 23 }

{ "docstring": "\n\t\t\t\tand lft >= \"{lft}\"\n\t\t\t\tand rgt <= \"{rgt}\"\n\t\t\t\n\t\t\tselect\n\t\t\t\tgl.account,\n\t\t\t\tgl.debit,\n\t\t\t\tgl.credit,\n\t\t\t\tgl.fiscal_year,\n\t\t\t\tMONTHNAME(gl.posting_date) as month_name,\n\t\t\t\tb.{budget_against} as budget_against\n\t\t\tfrom\n\t\t\t\t`tabGL Entry` gl,\n\t\t\t\t`tabBudget Account` ba,\n\t\t\t\t`tabBudget` b\n\t\t\twhere\n\t\t\t\tb.name = ba.parent\n\t\t\t\tand b.docstatus = 1\n\t\t\t\tand ba.account=gl.account\n\t\t\t\tand b.{budget_against} = gl.{budget_against}\n\t\t\t\tand gl.fiscal_year between %s and %s\n\t\t\t\tand b.{budget_against} = %s\n\t\t\t\tand exists(\n\t\t\t\t\tselect\n\t\t\t\t\t\tname\n\t\t\t\t\tfrom\n\t\t\t\t\t\t`tab{tab}`\n\t\t\t\t\twhere\n\t\t\t\t\t\tname = gl.{budget_against}\n\t\t\t\t\t\t{cond}\n\t\t\t\t)\n\t\t\t\tgroup by\n\t\t\t\t\tgl.name\n\t\t\t\torder by gl.fiscal_year\n\t\t", "language": "en", "n_whitespaces": 38, "n_words": 70, "vocab_size": 46 }

{ "docstring": "Prints out the index in proper format with title name and page number\r\n\r\n Args:\r\n toc (nested list): toc[1] - Topic name\r\n toc[2] - Page number\r\n ", "language": "en", "n_whitespaces": 64, "n_words": 25, "vocab_size": 22 }

{ "docstring": "\n This function draws a pie chart based on the `N` tensor `X`.\n\n The following `opts` are supported:\n\n - `opts.legend`: `list` containing legend names\n ", "language": "en", "n_whitespaces": 52, "n_words": 23, "vocab_size": 23 }

{ "docstring": "\n Prints out the pod definition that would be created by this operator.\n Does not include labels specific to the task instance (since there isn't\n one in a dry_run) and excludes all empty elements.\n ", "language": "en", "n_whitespaces": 62, "n_words": 33, "vocab_size": 32 }

{ "docstring": "\n Create a Continuous Random Variable with Exponential Power distribution.\n This distribution is known also as Generalized Normal\n distribution version 1.\n\n Explanation\n ===========\n\n The density of the Exponential Power distribution is given by\n\n .. math::\n f(x) := \\frac{\\beta}{2\\alpha\\Gamma(\\frac{1}{\\beta})}\n e^{{-(\\frac{|x - \\mu|}{\\alpha})^{\\beta}}}\n\n with :math:`x \\in [ - \\infty, \\infty ]`.\n\n Parameters\n ==========\n\n mu : Real number\n A location.\n alpha : Real number,`\\alpha > 0`\n A scale.\n beta : Real number, `\\beta > 0`\n A shape.\n\n Returns\n =======\n\n RandomSymbol\n\n Examples\n ========\n\n >>> from sympy.stats import ExponentialPower, density, cdf\n >>> from sympy import Symbol, pprint\n >>> z = Symbol(\"z\")\n >>> mu = Symbol(\"mu\")\n >>> alpha = Symbol(\"alpha\", positive=True)\n >>> beta = Symbol(\"beta\", positive=True)\n >>> X = ExponentialPower(\"x\", mu, alpha, beta)\n >>> pprint(density(X)(z), use_unicode=False)\n beta\n /|mu - z|\\\n -|--------|\n \\ alpha /\n beta*e\n ---------------------\n / 1 \\\n 2*alpha*Gamma|----|\n \\beta/\n >>> cdf(X)(z)\n 1/2 + lowergamma(1/beta, (Abs(mu - z)/alpha)**beta)*sign(-mu + z)/(2*gamma(1/beta))\n\n References\n ==========\n\n .. [1] https://reference.wolfram.com/language/ref/ExponentialPowerDistribution.html\n .. [2] https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1\n\n ", "language": "en", "n_whitespaces": 387, "n_words": 152, "vocab_size": 109 }

{ "docstring": " Returns the color map for visualizing the segmentation mask.\n Args:\n num_cls: Number of classes\n Returns:\n The color map\n ", "language": "en", "n_whitespaces": 42, "n_words": 18, "vocab_size": 15 }

{ "docstring": "\n Run net forward + backward in batches.\n\n Parameters\n ----------\n blobs: list of blobs to extract as in forward()\n diffs: list of diffs to extract as in backward()\n kwargs: Keys are input (for forward) and output (for backward) blob names\n and values are ndarrays. Refer to forward() and backward().\n Prefilled variants are called for lack of input or output blobs.\n\n Returns\n -------\n all_blobs: {blob name: blob ndarray} dict.\n all_diffs: {blob name: diff ndarray} dict.\n ", "language": "en", "n_whitespaces": 129, "n_words": 73, "vocab_size": 51 }

{ "docstring": "\n Tests that a lookup for a user that does not exist returns a 404\n ", "language": "en", "n_whitespaces": 29, "n_words": 14, "vocab_size": 11 }

{ "docstring": "Periodically sends a ping and checks if we should close the connection\n due to the other side timing out.\n ", "language": "en", "n_whitespaces": 33, "n_words": 19, "vocab_size": 18 }

{ "docstring": "Initialize the workflow logging context.\n\n Workflow executions are running as remote functions from\n WorkflowManagementActor. Without logging redirection, workflow\n inner execution logs will be pushed to the driver that initially\n created WorkflowManagementActor rather than the driver that\n actually submits the current workflow execution.\n We use this conext manager to re-configure the log files to send\n the logs to the correct driver, and to restore the log files once\n the execution is done.\n\n Args:\n job_id: The ID of the job that submits the workflow execution.\n ", "language": "en", "n_whitespaces": 120, "n_words": 83, "vocab_size": 56 }

{ "docstring": "\n Wrapper around `entry_point` so we can profile the code\n ", "language": "en", "n_whitespaces": 16, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Sets the callbacks configuration.\n\n Args:\n callbacks_class: Callbacks class, whose methods will be run during\n various phases of training and environment sample collection.\n See the `DefaultCallbacks` class and\n `examples/custom_metrics_and_callbacks.py` for more usage information.\n\n Returns:\n This updated TrainerConfig object.\n ", "language": "en", "n_whitespaces": 125, "n_words": 37, "vocab_size": 35 }

{ "docstring": "\n Tests the case when an organization was in EA/LA and has setup previously Dynamic Sampling rules,\n and now they have migrated to an AM2 plan, but haven't manipulated the bias toggles yet so they get the\n default biases. This also ensures that they no longer receive the deprecated dynamic sampling rules.\n ", "language": "en", "n_whitespaces": 80, "n_words": 51, "vocab_size": 44 }

{ "docstring": "Convert a string representation of truth to true (1) or false (0).\n\n True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values\n are 'n', 'no', 'f', 'false', 'off', and '0'. Raises ValueError if\n 'val' is anything else.\n ", "language": "en", "n_whitespaces": 52, "n_words": 39, "vocab_size": 35 }

{ "docstring": "\n Convert *x* using the unit type of the xaxis.\n\n If the artist is not contained in an Axes or if the xaxis does not\n have units, *x* itself is returned.\n ", "language": "en", "n_whitespaces": 59, "n_words": 30, "vocab_size": 24 }

{ "docstring": " Convenience property to retrieve the value tuple as a tuple of\n datetime objects.\n \n Initial or selected range.\n \n Initial or selected value, throttled to report only on mouseup.\n \n The minimum allowable value.\n \n The maximum allowable value.\n \n The step between consecutive values, in units of milliseconds.\n Default is one hour.\n ", "language": "en", "n_whitespaces": 101, "n_words": 48, "vocab_size": 38 }

{ "docstring": "\n Test that an analytics event is recorded when\n a first event with minified stack trace is received\n ", "language": "en", "n_whitespaces": 39, "n_words": 17, "vocab_size": 15 }

{ "docstring": "Create an ascending or descending array\n\n Returns evenly spaced values within the half-open interval\n ``[start, stop)`` as a one-dimensional array.\n ", "language": "en", "n_whitespaces": 41, "n_words": 20, "vocab_size": 20 }

{ "docstring": "Returns best PERSISTENT checkpoints, sorted by score.", "language": "en", "n_whitespaces": 6, "n_words": 7, "vocab_size": 7 }

{ "docstring": "\n Get NNI config directory.\n Create it if not exist.\n ", "language": "en", "n_whitespaces": 19, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n Test that the minimum batch size set in the config is used\n ", "language": "en", "n_whitespaces": 27, "n_words": 12, "vocab_size": 11 }

{ "docstring": "Swap system memory as a (total, used, free, sin, sout) tuple.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": " select name, bill_no from `tabPurchase Invoice`\n\t\twhere docstatus = 1 and bill_no is not null and bill_no != '' ", "language": "en", "n_whitespaces": 19, "n_words": 19, "vocab_size": 16 }

{ "docstring": "Determine the right endianness sign for a given DCE/RPC packet", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n Instantiate an object from the given model and field data. Functions as\n the complement to serialize_object().\n ", "language": "en", "n_whitespaces": 26, "n_words": 16, "vocab_size": 15 }

{ "docstring": "Component setup should fail if requirement can't install.", "language": "en", "n_whitespaces": 7, "n_words": 8, "vocab_size": 8 }

{ "docstring": "\n: int\n Floating point output precision in terms of number of places after the\n decimal, for regular formatting as well as scientific notation. Similar\n to ``precision`` in :meth:`numpy.set_printoptions`.\n\n: int\n Default space for DataFrame columns.\n\n: int\n If max_rows is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the height of the terminal and print a truncated object which fits\n the screen height. The IPython notebook, IPython qtconsole, or\n IDLE do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n\n: int\n The numbers of rows to show in a truncated view (when `max_rows` is\n exceeded). Ignored when `max_rows` is set to None or 0. When set to\n None, follows the value of `max_rows`.\n\n: int\n If max_cols is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the width of the terminal and print a truncated object which fits\n the screen width. The IPython notebook, IPython qtconsole, or IDLE\n do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n\n: int\n This sets the maximum number of categories pandas should output when\n printing out a `Categorical` or a Series of dtype \"category\".\n\n: int\n max_info_columns is used in DataFrame.info method to decide if\n per column information will be printed.\n\n: boolean\n When True, IPython notebook will use html representation for\n pandas objects (if it is available).\n\n: int\n Controls the number of nested levels to process when pretty-printing\n\n: boolean\n \"sparsify\" MultiIndex display (don't display repeated\n elements in outer levels within groups)\n\n: callable\n The callable should accept a floating point number and return\n a string with the desired format of the number. This is used\n in some places like SeriesFormatter.\n See formats.format.EngFormatter for an example.\n\n: int or None\n The maximum width in characters of a column in the repr of\n a pandas data structure. When the column overflows, a \"...\"\n placeholder is embedded in the output. A 'None' value means unlimited.\n\n: 'left'/'right'\n Controls the justification of column headers. used by DataFrameFormatter.\n\n: boolean\n Whether to print out the full DataFrame repr for wide DataFrames across\n multiple lines, `max_columns` is still respected, but the output will\n wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n\n: boolean or 'truncate'\n Whether to print out dimensions at the end of DataFrame repr.\n If 'truncate' is specified, only print out the dimensions if the\n frame is truncated (e.g. not display all rows and/or columns)\n\n: boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n\n: boolean\n Whether to handle Unicode characters belong to Ambiguous as Wide (width=2)\n (default: False)\n\n: boolean\n Whether to produce a latex DataFrame representation for jupyter\n environments that support it.\n (default: False)\n\n: boolean\n Whether to publish a Table Schema representation for frontends\n that support it.\n (default: False)\n\n: int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n\\\n: boolean\n When True, Jupyter notebook will process table contents using MathJax,\n rendering mathematical expressions enclosed by the dollar symbol.\n (default: True)\n\\\n: int\n The number of items that will be added to `dir(...)`. 'None' value means\n unlimited. Because dir is cached, changing this option will not immediately\n affect already existing dataframes until a column is deleted or added.\n\n This is for instance used to suggest columns from a dataframe to tab\n completion.\n\n: int\n Width of the display in characters. In case python/IPython is running in\n a terminal this can be set to None and pandas will correctly auto-detect\n the width.\n Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n terminal and hence it is not possible to correctly detect the width.\n\n: float or None\n if set to a float value, all float values smaller then the given threshold\n will be displayed as exactly 0 by repr and friends.\n\n: int or None\n When pretty-printing a long sequence, no more then `max_seq_items`\n will be printed. If items are omitted, they will be denoted by the\n addition of \"...\" to the resulting string.\n\n If set to None, the number of items to be printed is unlimited.\n\n: int or None\n df.info() will usually show null-counts for each column.\n For large frames this can be quite slow. max_info_rows and max_info_cols\n limit this null check only to frames with smaller dimensions than\n specified.\n\n: 'truncate'/'info'\n For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n show a truncated table (the default from 0.13), or switch to the view from\n df.info() (the behaviour in earlier versions of pandas).\n\n: bool, string or None\n This specifies if the memory usage of a DataFrame should be displayed when\n df.info() is called. Valid values True,False,'deep'\n\n: bool\n This specifies if the to_latex method of a Dataframe uses escapes special\n characters.\n Valid values: False,True\n\n:bool\n This specifies if the to_latex method of a Dataframe uses the longtable\n format.\n Valid values: False,True\n\n: bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n\n: string\n This specifies the format for multicolumn headers.\n Can be surrounded with '|'.\n Valid values: 'l', 'c', 'r', 'p{<width>}'\n\n: bool\n This specifies if the to_latex method of a Dataframe uses multirows\n to pretty-print MultiIndex rows.\n Valid values: False,True\n", "language": "en", "n_whitespaces": 1237, "n_words": 960, "vocab_size": 361 }

{ "docstring": "Twitter card tags should be used if nothing else is available.\n <html>\n <meta name=\"twitter:card\" content=\"summary\">\n <meta name=\"twitter:description\" content=\"Description\">\n <meta name=\"twitter:site\" content=\"@matrixdotorg\">\n </html>\n \n <html>\n <meta name=\"twitter:card\" content=\"summary\">\n <meta name=\"twitter:description\" content=\"Description\">\n <meta property=\"og:description\" content=\"Real Description\">\n <meta name=\"twitter:site\" content=\"@matrixdotorg\">\n <meta property=\"og:site_name\" content=\"matrix.org\">\n </html>\n ", "language": "en", "n_whitespaces": 139, "n_words": 40, "vocab_size": 25 }

{ "docstring": "Get matching tax rule(from_date is null or from_date <= '{posting_date}')\n\t\t\tand (to_date is null or to_date >= '{posting_date}')select * from `tabTax Rule`\n\t\twhere {0}", "language": "en", "n_whitespaces": 21, "n_words": 24, "vocab_size": 21 }

{ "docstring": "Return the wheel graph\n\n The wheel graph consists of a hub node connected to a cycle of (n-1) nodes.\n\n Parameters\n ----------\n n : int or iterable\n If an integer, node labels are 0 to n with center 0.\n If an iterable of nodes, the center is the first.\n create_using : NetworkX graph constructor, optional (default=nx.Graph)\n Graph type to create. If graph instance, then cleared before populated.\n\n Node labels are the integers 0 to n - 1.\n ", "language": "en", "n_whitespaces": 117, "n_words": 76, "vocab_size": 51 }

{ "docstring": "Get the most recent changes for this release by parsing\n HISTORY.rst file.\n ", "language": "en", "n_whitespaces": 18, "n_words": 12, "vocab_size": 12 }

{ "docstring": " Check that the selections holds valid data to display\n NB: len-as-condition is used as data could be a list or a numpy array\n\n Returns\n -------\n bool\n ``True` if there is data to be displayed, otherwise ``False``\n ", "language": "en", "n_whitespaces": 87, "n_words": 36, "vocab_size": 30 }

{ "docstring": "Check for random mac groups that have not been seen in a while and mark them as unavailable.", "language": "en", "n_whitespaces": 17, "n_words": 18, "vocab_size": 18 }

{ "docstring": "Restores the optimizers and learning rate scheduler states from the pre-loaded checkpoint.", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 11 }

{ "docstring": "\n Given a url remove the fragment and the authentication part.\n\n :rtype: str\n ", "language": "en", "n_whitespaces": 22, "n_words": 12, "vocab_size": 11 }

{ "docstring": "Tests that if we don't require approval for new accounts, newly created\n accounts are automatically marked as approved.\n ", "language": "en", "n_whitespaces": 32, "n_words": 18, "vocab_size": 18 }

{ "docstring": "Return a list of all the values for the named field.\n\n These will be sorted in the order they appeared in the original\n message, and may contain duplicates. Any fields deleted and\n re-inserted are always appended to the header list.\n\n If no such fields exist, failobj is returned (defaults to None).\n ", "language": "en", "n_whitespaces": 87, "n_words": 51, "vocab_size": 43 }

{ "docstring": "\n This tests that the privacy indicator on the private child pages explore view is set to \"PRIVATE\"\n ", "language": "en", "n_whitespaces": 32, "n_words": 17, "vocab_size": 16 }

{ "docstring": "Find one (or multiple) backend in a code line of the init.", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 12 }

{ "docstring": "View of `G` applying a filter on nodes and edges.\n\n `subgraph_view` provides a read-only view of the input graph that excludes\n nodes and edges based on the outcome of two filter functions `filter_node`\n and `filter_edge`.\n\n The `filter_node` function takes one argument --- the node --- and returns\n `True` if the node should be included in the subgraph, and `False` if it\n should not be included.\n\n The `filter_edge` function takes two (or three arguments if `G` is a\n multi-graph) --- the nodes describing an edge, plus the edge-key if\n parallel edges are possible --- and returns `True` if the edge should be\n included in the subgraph, and `False` if it should not be included.\n\n Both node and edge filter functions are called on graph elements as they\n are queried, meaning there is no up-front cost to creating the view.\n\n Parameters\n ----------\n G : networkx.Graph\n A directed/undirected graph/multigraph\n\n filter_node : callable, optional\n A function taking a node as input, which returns `True` if the node\n should appear in the view.\n\n filter_edge : callable, optional\n A function taking as input the two nodes describing an edge (plus the\n edge-key if `G` is a multi-graph), which returns `True` if the edge\n should appear in the view.\n\n Returns\n -------\n graph : networkx.Graph\n A read-only graph view of the input graph.\n\n Examples\n --------\n >>> G = nx.path_graph(6)\n\n Filter functions operate on the node, and return `True` if the node should\n appear in the view:\n\n >>> def filter_node(n1):\n ... return n1 != 5\n ...\n >>> view = nx.subgraph_view(G, filter_node=filter_node)\n >>> view.nodes()\n NodeView((0, 1, 2, 3, 4))\n\n We can use a closure pattern to filter graph elements based on additional\n data --- for example, filtering on edge data attached to the graph:\n\n >>> G[3][4][\"cross_me\"] = False\n >>> def filter_edge(n1, n2):\n ... return G[n1][n2].get(\"cross_me\", True)\n ...\n >>> view = nx.subgraph_view(G, filter_edge=filter_edge)\n >>> view.edges()\n EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)])\n\n >>> view = nx.subgraph_view(G, filter_node=filter_node, filter_edge=filter_edge,)\n >>> view.nodes()\n NodeView((0, 1, 2, 3, 4))\n >>> view.edges()\n EdgeView([(0, 1), (1, 2), (2, 3)])\n ", "language": "en", "n_whitespaces": 528, "n_words": 333, "vocab_size": 150 }

{ "docstring": "\n Warn on usage of ci= and convert to appropriate errorbar= arg.\n\n ci was deprecated when errorbar was added in 0.12. It should not be removed\n completely for some time, but it can be moved out of function definitions\n (and extracted from kwargs) after one cycle.\n\n ", "language": "en", "n_whitespaces": 61, "n_words": 45, "vocab_size": 42 }

{ "docstring": "\n Subtract other from self, and return a new masked array.\n\n ", "language": "en", "n_whitespaces": 25, "n_words": 10, "vocab_size": 10 }

{ "docstring": "Create a 2nd Mock Adam environment for testing exceptions.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n Plot a 3D wireframe.\n\n .. note::\n\n The *rcount* and *ccount* kwargs, which both default to 50,\n determine the maximum number of samples used in each direction. If\n the input data is larger, it will be downsampled (by slicing) to\n these numbers of points.\n\n Parameters\n ----------\n X, Y, Z : 2D arrays\n Data values.\n\n rcount, ccount : int\n Maximum number of samples used in each direction. If the input\n data is larger, it will be downsampled (by slicing) to these\n numbers of points. Setting a count to zero causes the data to be\n not sampled in the corresponding direction, producing a 3D line\n plot rather than a wireframe plot. Defaults to 50.\n\n rstride, cstride : int\n Downsampling stride in each direction. These arguments are\n mutually exclusive with *rcount* and *ccount*. If only one of\n *rstride* or *cstride* is set, the other defaults to 1. Setting a\n stride to zero causes the data to be not sampled in the\n corresponding direction, producing a 3D line plot rather than a\n wireframe plot.\n\n 'classic' mode uses a default of ``rstride = cstride = 1`` instead\n of the new default of ``rcount = ccount = 50``.\n\n **kwargs\n Other arguments are forwarded to `.Line3DCollection`.\n ", "language": "en", "n_whitespaces": 474, "n_words": 198, "vocab_size": 105 }

{ "docstring": "\n Raise an error if the view returned None or an uncalled coroutine.\n ", "language": "en", "n_whitespaces": 27, "n_words": 12, "vocab_size": 11 }

{ "docstring": "Create empty DataFrame or Series which has correct dtype", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n Iterate over the rows of the table, with each row returned as a list of BoundBlocks\n ", "language": "en", "n_whitespaces": 31, "n_words": 16, "vocab_size": 14 }

{ "docstring": "\n Create a continuous random variable with a uniform distribution.\n\n Explanation\n ===========\n\n The density of the uniform distribution is given by\n\n .. math::\n f(x) := \\begin{cases}\n \\frac{1}{b - a} & \\text{for } x \\in [a,b] \\\\\n 0 & \\text{otherwise}\n \\end{cases}\n\n with :math:`x \\in [a,b]`.\n\n Parameters\n ==========\n\n a : Real number, :math:`-\\infty < a`, the left boundary\n b : Real number, :math:`a < b < \\infty`, the right boundary\n\n Returns\n =======\n\n RandomSymbol\n\n Examples\n ========\n\n >>> from sympy.stats import Uniform, density, cdf, E, variance\n >>> from sympy import Symbol, simplify\n\n >>> a = Symbol(\"a\", negative=True)\n >>> b = Symbol(\"b\", positive=True)\n >>> z = Symbol(\"z\")\n\n >>> X = Uniform(\"x\", a, b)\n\n >>> density(X)(z)\n Piecewise((1/(-a + b), (b >= z) & (a <= z)), (0, True))\n\n >>> cdf(X)(z)\n Piecewise((0, a > z), ((-a + z)/(-a + b), b >= z), (1, True))\n\n >>> E(X)\n a/2 + b/2\n\n >>> simplify(variance(X))\n a**2/12 - a*b/6 + b**2/12\n\n References\n ==========\n\n .. [1] https://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29\n .. [2] http://mathworld.wolfram.com/UniformDistribution.html\n\n ", "language": "en", "n_whitespaces": 331, "n_words": 157, "vocab_size": 111 }

{ "docstring": "ttext = <matches _ttext_matcher>\n\n We allow any non-TOKEN_ENDS in ttext, but add defects to the token's\n defects list if we find non-ttext characters. We also register defects for\n *any* non-printables even though the RFC doesn't exclude all of them,\n because we follow the spirit of RFC 5322.\n\n ", "language": "en", "n_whitespaces": 63, "n_words": 47, "vocab_size": 39 }

{ "docstring": " Get instance's attributes as dict of keyword arguments.\n\n Parameters\n ==========\n\n exclude : collection of str\n Collection of keywords to exclude.\n\n apply : callable, optional\n Function to apply to all values.\n ", "language": "en", "n_whitespaces": 88, "n_words": 30, "vocab_size": 24 }

{ "docstring": "Does not call into anything or produce a new value, as the time\n this function gets called, all child nodes are already resolved to\n ObjectRefs.\n ", "language": "en", "n_whitespaces": 46, "n_words": 25, "vocab_size": 25 }

{ "docstring": "\n Check that the {{ settings(use_default_site=True) }} option works with\n no site in the context\n ", "language": "en", "n_whitespaces": 36, "n_words": 14, "vocab_size": 13 }

{ "docstring": "\nInput #0, mpeg, from 'clip.mp4':\n Duration: 00:02:15.00, start: 52874.498178, bitrate: 266 kb/s\n Stream #0:0[0x1e0](eng): Video: ..., 25 tbr, 90k tbn, 50 tbc\n Stream #0:1[0x1c0](und): Audio: mp2, 0 channels, s16p\nAt least one output file must be specified", "language": "en", "n_whitespaces": 42, "n_words": 37, "vocab_size": 36 }

{ "docstring": "\n Wait until the flag has been set.\n\n If the flag has already been set when this method is called, it returns immediately.\n ", "language": "en", "n_whitespaces": 44, "n_words": 22, "vocab_size": 18 }

{ "docstring": "\n The principle method on agents. Queries for scheduled flow runs and submits\n them for execution in parallel.\n ", "language": "en", "n_whitespaces": 39, "n_words": 17, "vocab_size": 16 }

{ "docstring": "Determines output length of a convolution given input length.\n\n Args:\n input_length: integer.\n filter_size: integer.\n padding: one of \"same\", \"valid\", \"full\", \"causal\"\n stride: integer.\n dilation: dilation rate, integer.\n\n Returns:\n The output length (integer).\n ", "language": "en", "n_whitespaces": 83, "n_words": 32, "vocab_size": 26 }

{ "docstring": "\n Return all available next segments in a split cable path.\n ", "language": "en", "n_whitespaces": 25, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n Return a list of template names to be used for the request. Must return\n a list. May not be called if render_to_response is overridden.\n ", "language": "en", "n_whitespaces": 46, "n_words": 24, "vocab_size": 22 }

{ "docstring": "Check if we should remove lines, then do it.\n Return the number of lines removed.\n ", "language": "en", "n_whitespaces": 21, "n_words": 15, "vocab_size": 15 }

{ "docstring": "get one trial job, i.e., one hyperparameter configuration.\n\n If this function is called, Command will be sent by BOHB:\n a. If there is a parameter need to run, will return \"NewTrialJob\" with a dict:\n {\n 'parameter_id': id of new hyperparameter\n 'parameter_source': 'algorithm'\n 'parameters': value of new hyperparameter\n }\n b. If BOHB don't have parameter waiting, will return \"NoMoreTrialJobs\" with\n {\n 'parameter_id': '-1_0_0',\n 'parameter_source': 'algorithm',\n 'parameters': ''\n }\n ", "language": "en", "n_whitespaces": 189, "n_words": 67, "vocab_size": 48 }

{ "docstring": "Converts a global `jax.Array` to a host local `jax.Array`.\n\n You can use this function to transition to `jax.Array`. Using `jax.Array` with\n `pjit` has the same semantics of using GDA with pjit i.e. all `jax.Array`\n inputs to pjit should be globally shaped and the output from `pjit` will also\n be globally shaped `jax.Array`s\n\n You can use this function to convert the globally shaped `jax.Array` output\n from pjit to host local values again so that the transition to jax.Array can\n be a mechanical change.\n\n Example usage:\n\n ```\n global_inputs = jax.experimental.pjit.host_local_array_to_global_array(\n host_local_inputs, global_mesh, in_pspecs)\n\n with mesh:\n global_out = pjitted_fun(global_inputs)\n\n host_local_output = jax.experimental.pjit.global_array_to_host_local_array(\n global_out, mesh, out_pspecs)\n ```\n\n Args:\n global_inputs: A Pytree of global `jax.Array`s.\n global_mesh: The global mesh.\n pspecs: A Pytree of PartitionSpecs.\n ", "language": "en", "n_whitespaces": 152, "n_words": 119, "vocab_size": 73 }

{ "docstring": "\n Override of __setitem__ to track keys and simulate Python2.7 dict\n\n Parameters\n ----------\n key: hashable\n value: Any\n ", "language": "en", "n_whitespaces": 59, "n_words": 16, "vocab_size": 16 }

{ "docstring": "Returns a boolean, whether running in venv with no system site-packages.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n Returns all values to index as \"autocomplete\". This is the value of all AutocompleteFields\n ", "language": "en", "n_whitespaces": 29, "n_words": 14, "vocab_size": 13 }

{ "docstring": "Datasets will stack the list of tensor, so switch them to tuples.", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 12 }

{ "docstring": "A value not used for task-mapping should not be recorded.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 9 }

{ "docstring": "Implements support for handling RaggedTensors.\n\n Args:\n y_true: RaggedTensor truth values. shape = `[batch_size, d0, .. dN]`.\n y_pred: RaggedTensor predicted values. shape = `[batch_size, d0, .. dN]`.\n\n Returns:\n Mean squared error values. shape = `[batch_size, d0, .. dN-1]`.\n When the number of dimensions of the batch feature vector [d0, .. dN] is\n greater than one the return value is a RaggedTensor. Otherwise a Dense\n tensor with dimensions [batch_size] is returned.\n ", "language": "en", "n_whitespaces": 108, "n_words": 69, "vocab_size": 47 }

{ "docstring": "Test formatting does not allow extra key word arguments.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Opposite of the Local Outlier Factor of X.\n\n It is the opposite as bigger is better, i.e. large values correspond\n to inliers.\n\n **Only available for novelty detection (when novelty is set to True).**\n The argument X is supposed to contain *new data*: if X contains a\n point from training, it considers the later in its own neighborhood.\n Also, the samples in X are not considered in the neighborhood of any\n point. Because of this, the scores obtained via ``score_samples`` may\n differ from the standard LOF scores.\n The standard LOF scores for the training data is available via the\n ``negative_outlier_factor_`` attribute.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The query sample or samples to compute the Local Outlier Factor\n w.r.t. the training samples.\n\n Returns\n -------\n opposite_lof_scores : ndarray of shape (n_samples,)\n The opposite of the Local Outlier Factor of each input samples.\n The lower, the more abnormal.\n ", "language": "en", "n_whitespaces": 311, "n_words": 148, "vocab_size": 93 }

{ "docstring": "\n\t\tselect name, start_date, end_date\n\t\tfrom `tabPayroll Period`\n\t\twhere\n\t\t\tcompany=%(company)s\n\t\t\tand %(start_date)s between start_date and end_date\n\t\t\tand %(end_date)s between start_date and end_date\n\t", "language": "en", "n_whitespaces": 15, "n_words": 21, "vocab_size": 14 }

{ "docstring": "Returns density of bipartite graph B.\n\n Parameters\n ----------\n B : NetworkX graph\n\n nodes: list or container\n Nodes in one node set of the bipartite graph.\n\n Returns\n -------\n d : float\n The bipartite density\n\n Examples\n --------\n >>> from networkx.algorithms import bipartite\n >>> G = nx.complete_bipartite_graph(3, 2)\n >>> X = set([0, 1, 2])\n >>> bipartite.density(G, X)\n 1.0\n >>> Y = set([3, 4])\n >>> bipartite.density(G, Y)\n 1.0\n\n Notes\n -----\n The container of nodes passed as argument must contain all nodes\n in one of the two bipartite node sets to avoid ambiguity in the\n case of disconnected graphs.\n See :mod:`bipartite documentation <networkx.algorithms.bipartite>`\n for further details on how bipartite graphs are handled in NetworkX.\n\n See Also\n --------\n color\n ", "language": "en", "n_whitespaces": 208, "n_words": 113, "vocab_size": 79 }

{ "docstring": "\n Some endpoints are called by javascript,\n returning json will allow us to more elegantly handle side-effects in-page\n ", "language": "en", "n_whitespaces": 27, "n_words": 17, "vocab_size": 17 }