Datasets:

semeru

/

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

like 0

{ "docstring": "\n Returns the root SymPy directory and set the global value\n indicating whether the system is case sensitive or not.\n ", "language": "en", "n_whitespaces": 29, "n_words": 19, "vocab_size": 17 }

{ "docstring": "Checks for calls to C random functions (rand, rand_r, random, ...).\n\n Caffe code should (almost) always use the caffe_rng_* functions rather\n than these, as the internal state of these C functions is independent of the\n native Caffe RNG system which should produce deterministic results for a\n fixed Caffe seed set using Caffe::set_random_seed(...).\n\n Args:\n filename: The name of the current file.\n clean_lines: A CleansedLines instance containing the file.\n linenum: The number of the line to check.\n error: The function to call with any errors found.\n ", "language": "en", "n_whitespaces": 102, "n_words": 84, "vocab_size": 64 }

{ "docstring": "\n End-to-end test of a simple mapped dag.\n\n We test with multiple executors as they have different \"execution environments\" -- for instance\n DebugExecutor runs a lot more in the same process than other Executors.\n\n ", "language": "en", "n_whitespaces": 62, "n_words": 33, "vocab_size": 31 }

{ "docstring": "Test http can startup with an emergency self signed cert when the current one is broken.", "language": "en", "n_whitespaces": 15, "n_words": 16, "vocab_size": 16 }

{ "docstring": "\n Return a dictionary of request.META with sensitive values redacted.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Overrides this method to set the :attr:`device`\n\n Returns:\n nn.Module: The model itself.\n ", "language": "en", "n_whitespaces": 37, "n_words": 12, "vocab_size": 12 }

{ "docstring": "\n Returns the name of the attribute that should be used to store\n a reference to the fetched/created object on a request.\n ", "language": "en", "n_whitespaces": 43, "n_words": 21, "vocab_size": 17 }

{ "docstring": "Prefetch related fields that are needed to process the mutation.\n\n If we are updating an instance and want to update its attributes,\n # prefetch them.\n ", "language": "en", "n_whitespaces": 46, "n_words": 25, "vocab_size": 23 }

{ "docstring": "\n Whether we can use the fastpaths implement in _libs.join\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n `sub_syllables` returns the subword of the associative word `self` that\n consists of syllables from positions `from_to` to `to_j`, where\n `from_to` and `to_j` must be positive integers and indexing is done\n with origin 0.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import free_group\n >>> f, a, b = free_group(\"a, b\")\n >>> w = a**5*b*a**2*b**-4*a\n >>> w.sub_syllables(1, 2)\n b\n >>> w.sub_syllables(3, 3)\n <identity>\n\n ", "language": "en", "n_whitespaces": 158, "n_words": 59, "vocab_size": 48 }

{ "docstring": "\n Set how floating-point errors are handled.\n\n Note that operations on integer scalar types (such as `int16`) are\n handled like floating point, and are affected by these settings.\n\n Parameters\n ----------\n all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Set treatment for all types of floating-point errors at once:\n\n - ignore: Take no action when the exception occurs.\n - warn: Print a `RuntimeWarning` (via the Python `warnings` module).\n - raise: Raise a `FloatingPointError`.\n - call: Call a function specified using the `seterrcall` function.\n - print: Print a warning directly to ``stdout``.\n - log: Record error in a Log object specified by `seterrcall`.\n\n The default is not to change the current behavior.\n divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for division by zero.\n over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for floating-point overflow.\n under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for floating-point underflow.\n invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for invalid floating-point operation.\n\n Returns\n -------\n old_settings : dict\n Dictionary containing the old settings.\n\n See also\n --------\n seterrcall : Set a callback function for the 'call' mode.\n geterr, geterrcall, errstate\n\n Notes\n -----\n The floating-point exceptions are defined in the IEEE 754 standard [1]_:\n\n - Division by zero: infinite result obtained from finite numbers.\n - Overflow: result too large to be expressed.\n - Underflow: result so close to zero that some precision\n was lost.\n - Invalid operation: result is not an expressible number, typically\n indicates that a NaN was produced.\n\n .. [1] https://en.wikipedia.org/wiki/IEEE_754\n\n Examples\n --------\n >>> old_settings = np.seterr(all='ignore') #seterr to known value\n >>> np.seterr(over='raise')\n {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}\n >>> np.seterr(**old_settings) # reset to default\n {'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}\n\n >>> np.int16(32000) * np.int16(3)\n 30464\n >>> old_settings = np.seterr(all='warn', over='raise')\n >>> np.int16(32000) * np.int16(3)\n Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\n FloatingPointError: overflow encountered in scalar multiply\n\n >>> old_settings = np.seterr(all='print')\n >>> np.geterr()\n {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}\n >>> np.int16(32000) * np.int16(3)\n 30464\n\n ", "language": "en", "n_whitespaces": 577, "n_words": 336, "vocab_size": 195 }

{ "docstring": "\n Create drawables for the token content.\n ", "language": "en", "n_whitespaces": 21, "n_words": 6, "vocab_size": 6 }

{ "docstring": "Return a monotonic time in seconds.\n\n This is the coarse version of time_monotonic, which is faster but less accurate.\n\n Since many arm64 and 32-bit platforms don't support VDSO with time.monotonic\n because of errata, we can't rely on the kernel to provide a fast\n monotonic time.\n\n https://lore.kernel.org/lkml/20170404171826.25030-1-marc.zyngier@arm.com/\n ", "language": "en", "n_whitespaces": 64, "n_words": 46, "vocab_size": 41 }

{ "docstring": "This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,\n or a list of PyTorch tensors if one specifies torchify=True.\n ", "language": "en", "n_whitespaces": 34, "n_words": 28, "vocab_size": 18 }

{ "docstring": "A wrapper of `update_step` to enable XLA acceleration.\n\n Due to `tf.function` tracing mechanism, for (gradient, variable) pairs of\n the same shape and dtype, the execution graph always invoke the first\n pair it has seen. Thus, we need a `key` argument to make each\n (gradient, variable) pair unique. In additions, XLA cannot understand\n string input, so the key is an integer.\n\n Args:\n gradient: backpropagated gradient of the given variable.\n variable: variable whose value needs to be updated.\n key (int): a unique key that identifies the variable.\n\n Returns:\n An `Operation` that applies the specified gradients.\n ", "language": "en", "n_whitespaces": 185, "n_words": 93, "vocab_size": 73 }

{ "docstring": "\n Logs in the wallet with a specific key.\n ", "language": "en", "n_whitespaces": 23, "n_words": 8, "vocab_size": 8 }

{ "docstring": "Tomasz [pushed](https://bitbucket.org/kolaszek/repository-name/branch/master) 10 commits to branch master. Commits by Tomasz (4), James (3), Brendon (2) and others (1).\\n\\n{commit_info*9}* first commit ([84b96adc644](https://bitbucket.org/kolaszek/repository-name/commits/84b96adc644a30fd6465b3d196369d880762afed))", "language": "en", "n_whitespaces": 20, "n_words": 21, "vocab_size": 20 }

{ "docstring": "Override drawControl to draw odd tabs in a different color.\n\n Draws the given element with the provided painter with the style\n options specified by option.\n\n Args:\n element: ControlElement\n opt: QStyleOption\n p: QPainter\n widget: QWidget\n ", "language": "en", "n_whitespaces": 106, "n_words": 34, "vocab_size": 31 }

{ "docstring": "Generate a square mask for the sequence. The masked positions are filled with float('-inf').\n Unmasked positions are filled with float(0.0).\n ", "language": "en", "n_whitespaces": 30, "n_words": 20, "vocab_size": 16 }

{ "docstring": "Test that config options that are removed in a file are also removed\n from our _config_options dict.\n [theme]\n base = \"dark\"\n font = \"sans serif\"\n \n [theme]\n base = \"dark\"\n ", "language": "en", "n_whitespaces": 86, "n_words": 29, "vocab_size": 21 }

{ "docstring": "\n Override numpy.ndarray's __getitem__ method to work as desired.\n\n This function adds lists and Series as valid boolean indexers\n (ndarrays only supports ndarray with dtype=bool).\n\n If resulting ndim != 1, plain ndarray is returned instead of\n corresponding `Index` subclass.\n\n ", "language": "en", "n_whitespaces": 81, "n_words": 38, "vocab_size": 36 }

{ "docstring": "\n\t\tSELECT\n\t\t\tso.transaction_date as date,\n\t\t\tsoi.delivery_date as delivery_date,\n\t\t\tso.name as sales_order,\n\t\t\tso.status, so.customer, soi.item_code,\n\t\t\tDATEDIFF(CURDATE(), soi.delivery_date) as delay_days,\n\t\t\tIF(so.status in ('Completed','To Bill'), 0, (SELECT delay_days)) as delay,\n\t\t\tsoi.qty, soi.delivered_qty,\n\t\t\t(soi.qty - soi.delivered_qty) AS pending_qty,\n\t\t\tIF((SELECT pending_qty) = 0, (TO_SECONDS(Max(dn.posting_date))-TO_SECONDS(so.transaction_date)), 0) as time_taken_to_deliver,\n\t\t\tIFNULL(SUM(sii.qty), 0) as billed_qty,\n\t\t\tsoi.base_amount as amount,\n\t\t\t(soi.delivered_qty * soi.base_rate) as delivered_qty_amount,\n\t\t\t(soi.billed_amt * IFNULL(so.conversion_rate, 1)) as billed_amount,\n\t\t\t(soi.base_amount - (soi.billed_amt * IFNULL(so.conversion_rate, 1))) as pending_amount,\n\t\t\tsoi.warehouse as warehouse,\n\t\t\tso.company, soi.name,\n\t\t\tsoi.description as description\n\t\tFROM\n\t\t\t`tabSales Order` so,\n\t\t\t(`tabSales Order Item` soi\n\t\tLEFT JOIN `tabSales Invoice Item` sii\n\t\t\tON sii.so_detail = soi.name and sii.docstatus = 1)\n\t\tLEFT JOIN `tabDelivery Note Item` dni\n\t\t\ton dni.so_detail = soi.name\n\t\tRIGHT JOIN `tabDelivery Note` dn\n\t\t\ton dni.parent = dn.name and dn.docstatus = 1\n\t\tWHERE\n\t\t\tsoi.parent = so.name\n\t\t\tand so.status not in ('Stopped', 'Closed', 'On Hold')\n\t\t\tand so.docstatus = 1\n\t\t\t{conditions}\n\t\tGROUP BY soi.name\n\t\tORDER BY so.transaction_date ASC, soi.item_code ASC\n\t", "language": "en", "n_whitespaces": 112, "n_words": 146, "vocab_size": 102 }

{ "docstring": "\n Get the mapping of coroutine 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": "Computes the full Hessian matrix for the scalar-valued f(*params).\n\n Args:\n f: A function taking `params` and returning a scalar.\n params: A possibly nested structure of tensors.\n use_pfor: If true, uses `tf.vectorized_map` calls instead of looping.\n dtype: Required if `use_pfor=False`. A possibly nested structure of dtypes\n (e.g. `tf.float32`) matching the structure of `f`'s returns.\n\n Returns:\n A possibly nested structure of matrix slices corresponding to `params`. Each\n slice has shape [P, p_s] where `p_s` is the number of parameters (`tf.size`)\n in the corresponding element of `params` and `P` is the total number of\n parameters (`sum_s(p_s)`). The full matrix can be obtained by concatenating\n along the second axis.\n ", "language": "en", "n_whitespaces": 166, "n_words": 105, "vocab_size": 73 }

{ "docstring": "Probability estimates.\n\n This method is only available for log loss and modified Huber loss.\n\n Multiclass probability estimates are derived from binary (one-vs.-rest)\n estimates by simple normalization, as recommended by Zadrozny and\n Elkan.\n\n Binary probability estimates for loss=\"modified_huber\" are given by\n (clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions\n it is necessary to perform proper probability calibration by wrapping\n the classifier with\n :class:`~sklearn.calibration.CalibratedClassifierCV` instead.\n\n Parameters\n ----------\n X : {array-like, sparse matrix}, shape (n_samples, n_features)\n Input data for prediction.\n\n Returns\n -------\n ndarray of shape (n_samples, n_classes)\n Returns the probability of the sample for each class in the model,\n where classes are ordered as they are in `self.classes_`.\n\n References\n ----------\n Zadrozny and Elkan, \"Transforming classifier scores into multiclass\n probability estimates\", SIGKDD'02,\n https://dl.acm.org/doi/pdf/10.1145/775047.775151\n\n The justification for the formula in the loss=\"modified_huber\"\n case is in the appendix B in:\n http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf\n ", "language": "en", "n_whitespaces": 339, "n_words": 138, "vocab_size": 98 }

{ "docstring": "Fetch JSON Web Key Sets from a provider.\n\n Fetched keys will be stored in the cache to the reduced amount of possible\n requests.\n :raises AuthenticationError\n ", "language": "en", "n_whitespaces": 37, "n_words": 25, "vocab_size": 24 }