function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def __init__(self):
self.status = c_api.TF_NewStatus() | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def __init__(self):
self.graph = c_api.TF_NewGraph()
# Note: when we're destructing the global context (i.e when the process is
# terminating) we may have already deleted other modules. By capturing the
# DeleteGraph function here, we retain the ability to cleanly destroy the
# graph at shutdown, wh... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def __init__(self):
self.options = c_api.TF_NewImportGraphDefOptions() | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def __init__(self, results):
self.results = results | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def __init__(self, func):
self.func = func
# Note: when we're destructing the global context (i.e when the process is
# terminating) we may have already deleted other modules. By capturing the
# DeleteFunction function here, we retain the ability to cleanly destroy the
# Function at shutdown, which ... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def has_been_garbage_collected(self):
return self.func is None | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def __init__(self, buf_string):
self.buffer = c_api.TF_NewBufferFromString(compat.as_bytes(buf_string)) | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def __init__(self):
op_def_proto = op_def_pb2.OpList()
buf = c_api.TF_GetAllOpList()
try:
op_def_proto.ParseFromString(c_api.TF_GetBuffer(buf))
self._api_def_map = c_api.TF_NewApiDefMap(buf)
finally:
c_api.TF_DeleteBuffer(buf)
self._op_per_name = {}
for op in op_def_proto.op:
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def put_api_def(self, text):
c_api.TF_ApiDefMapPut(self._api_def_map, text, len(text)) | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def get_op_def(self, op_name):
if op_name in self._op_per_name:
return self._op_per_name[op_name]
raise ValueError(f"No op_def found for op name {op_name}.") | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def tf_buffer(data=None):
"""Context manager that creates and deletes TF_Buffer.
Example usage:
with tf_buffer() as buf:
# get serialized graph def into buf
...
proto_data = c_api.TF_GetBuffer(buf)
graph_def.ParseFromString(compat.as_bytes(proto_data))
# buf has been deleted
wi... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def tf_operations(graph):
"""Generator that yields every TF_Operation in `graph`.
Args:
graph: Graph
Yields:
wrapped TF_Operation
"""
# pylint: disable=protected-access
pos = 0
c_op, pos = c_api.TF_GraphNextOperation(graph._c_graph, pos)
while c_op is not None:
yield c_op
c_op, pos = c... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def setUp(self):
self.asn1Spec = rfc2314.CertificationRequest() | catapult-project/catapult | [
1835,
570,
1835,
1039,
1429033745
] |
def add_arguments(self, parser):
"""
Add arguments to the command parser.
"""
parser.add_argument(
'--course-id', '--course_id',
dest='course_ids',
action='append',
help=u'Migrates transcripts for the list of courses.'
)
par... | Stanford-Online/edx-platform | [
41,
19,
41,
1,
1374606346
] |
def _get_migration_options(self, options):
"""
Returns the command arguments configured via django admin.
"""
force_update = options['force_update']
commit = options['commit']
courses_mode = get_mutually_exclusive_required_option(options, 'course_ids', 'all_courses', 'fro... | Stanford-Online/edx-platform | [
41,
19,
41,
1,
1374606346
] |
def main():
opts, args = getopt.getopt(sys.argv[1:], 'D:U:')
for o, a in opts:
if o == '-D':
defs.append(a)
if o == '-U':
undefs.append(a)
if not args:
args = ['-']
for filename in args:
if filename == '-':
process(sys.stdin,... | google/google-ctf | [
3196,
457,
3196,
1,
1524844563
] |
def process(fpi, fpo):
keywords = ('if', 'ifdef', 'ifndef', 'else', 'endif')
ok = 1
stack = []
while 1:
line = fpi.readline()
if not line: break
while line[-2:] == '\\\n':
nextline = fpi.readline()
if not nextline: break
line = line +... | google/google-ctf | [
3196,
457,
3196,
1,
1524844563
] |
def uart_tx():
# fmt: off
# Block with TX deasserted until data available
pull()
# Initialise bit counter, assert start bit for 8 cycles
set(x, 7) .side(0) [7]
# Shift out 8 data bits, 8 execution cycles per bit
label("bitloop")
out(pins, 1) [6]
jmp(x_dec, "bitloo... | pfalcon/micropython | [
741,
72,
741,
28,
1388317127
] |
def pio_uart_print(sm, s):
for c in s:
sm.put(ord(c)) | pfalcon/micropython | [
741,
72,
741,
28,
1388317127
] |
def assumed_state(self):
"""Return True if unable to access real state of entity."""
return self.gateway.optimistic | tchellomello/home-assistant | [
7,
1,
7,
6,
1467778429
] |
def is_closed(self):
"""Return True if cover is closed."""
set_req = self.gateway.const.SetReq
if set_req.V_DIMMER in self._values:
return self._values.get(set_req.V_DIMMER) == 0
return self._values.get(set_req.V_LIGHT) == STATE_OFF | tchellomello/home-assistant | [
7,
1,
7,
6,
1467778429
] |
def current_cover_position(self):
"""Return current position of cover.
None is unknown, 0 is closed, 100 is fully open.
"""
set_req = self.gateway.const.SetReq
return self._values.get(set_req.V_DIMMER) | tchellomello/home-assistant | [
7,
1,
7,
6,
1467778429
] |
def __init__(self, **kwargs):
super(_Merge, self).__init__(**kwargs)
self.supports_masking = True | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _compute_elemwise_op_output_shape(self, shape1, shape2):
"""Computes the shape of the resultant of an elementwise operation.
Arguments:
shape1: tuple or None. Shape of the first tensor
shape2: tuple or None. Shape of the second tensor
Returns:
expected output shape when an elem... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def call(self, inputs):
if self._reshape_required:
reshaped_inputs = []
input_ndims = list(map(K.ndim, inputs))
if None not in input_ndims:
# If ranks of all inputs are available,
# we simply expand each of them at axis=1
# until all of them have the same rank.
max_... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The l... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _merge_function(self, inputs):
output = inputs[0]
for i in range(1, len(inputs)):
output += inputs[i]
return output | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _merge_function(self, inputs):
output = inputs[0]
for i in range(1, len(inputs)):
output *= inputs[i]
return output | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _merge_function(self, inputs):
output = inputs[0]
for i in range(1, len(inputs)):
output += inputs[i]
return output / len(inputs) | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _merge_function(self, inputs):
output = inputs[0]
for i in range(1, len(inputs)):
output = K.maximum(output, inputs[i])
return output | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def __init__(self, axis=-1, **kwargs):
super(Concatenate, self).__init__(**kwargs)
self.axis = axis
self.supports_masking = True | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def call(self, inputs):
if not isinstance(inputs, list):
raise ValueError('A `Concatenate` layer should be called '
'on a list of inputs.')
return K.concatenate(inputs, axis=self.axis) | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The l... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def __init__(self, axes, normalize=False, **kwargs):
super(Dot, self).__init__(**kwargs)
if not isinstance(axes, int):
if not isinstance(axes, (list, tuple)):
raise TypeError('Invalid type for `axes` - '
'should be a list or an int.')
if len(axes) != 2:
raise ... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def call(self, inputs):
x1 = inputs[0]
x2 = inputs[1]
if isinstance(self.axes, int):
if self.axes < 0:
axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)]
else:
axes = [self.axes] * 2
else:
axes = []
for i in range(len(self.axes)):
if self.axes[i] < 0:... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def compute_mask(self, inputs, mask=None):
return None | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def add(inputs, **kwargs):
"""Functional interface to the `Add` layer.
Arguments:
inputs: A list of input tensors (at least 2).
**kwargs: Standard layer keyword arguments.
Returns:
A tensor, the sum of the inputs.
"""
return Add(**kwargs)(inputs) | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def average(inputs, **kwargs):
"""Functional interface to the `Average` layer.
Arguments:
inputs: A list of input tensors (at least 2).
**kwargs: Standard layer keyword arguments.
Returns:
A tensor, the average of the inputs.
"""
return Average(**kwargs)(inputs) | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def concatenate(inputs, axis=-1, **kwargs):
"""Functional interface to the `Concatenate` layer.
Arguments:
inputs: A list of input tensors (at least 2).
axis: Concatenation axis.
**kwargs: Standard layer keyword arguments.
Returns:
A tensor, the concatenation of the inputs alongside axis... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def __init__(self, fp):
self.palette = [(i, i, i) for i in range(256)]
while True:
s = fp.readline()
if not s:
break
if s[0:1] == b"#":
continue
if len(s) > 100:
raise SyntaxError("bad palette file")
... | Microvellum/Fluid-Designer | [
69,
30,
69,
37,
1461884765
] |
def __init__(self):
self._num_rows = 3
self._num_columns = 2
self._data = [["hello" for j in range(self._num_columns)] for i in range(self._num_rows)] | jeremiedecock/snippets | [
20,
6,
20,
1,
1433499549
] |
def get_num_columns(self):
return self._num_columns | jeremiedecock/snippets | [
20,
6,
20,
1,
1433499549
] |
def set_data(self, row_index, column_index, value):
self._data[row_index][column_index] = value | jeremiedecock/snippets | [
20,
6,
20,
1,
1433499549
] |
def __init__(self, data, parent=None):
super().__init__(parent)
self._data = data # DON'T CALL THIS ATTRIBUTE "data", A QAbstractItemModel METHOD ALREADY HAVE THIS NAME (model.data(index, role)) !!! | jeremiedecock/snippets | [
20,
6,
20,
1,
1433499549
] |
def columnCount(self, parent):
return self._data.get_num_columns() | jeremiedecock/snippets | [
20,
6,
20,
1,
1433499549
] |
def setData(self, index, value, role):
if role == Qt.EditRole:
try:
self._data.set_data(index.row(), index.column(), value)
# The following line are necessary e.g. to dynamically update the QSortFilterProxyModel
self.dataChanged.emit(index, index, [Q... | jeremiedecock/snippets | [
20,
6,
20,
1,
1433499549
] |
def changedCallback():
print("changed") | jeremiedecock/snippets | [
20,
6,
20,
1,
1433499549
] |
def _redirect_event_creation(category_id, event_type):
anchor = f'create-event:{event_type}:{category_id}'
return redirect(url_for('.display', category_id=category_id, _anchor=anchor)) | indico/indico | [
1446,
358,
1446,
649,
1311774990
] |
def _redirect_to_bootstrap():
# No users in Indico yet? Redirect from index page to bootstrap form
if (request.endpoint == 'categories.display' and not request.view_args['category_id'] and
not User.query.filter_by(is_system=False).has_rows()):
return redirect(url_for('bootstrap.index')) | indico/indico | [
1446,
358,
1446,
649,
1311774990
] |
def __init__(self, x):
self.val = x
self.left = None
self.right = None | jiadaizhao/LeetCode | [
39,
21,
39,
2,
1502171846
] |
def __init__(self, obj):
return obj | thonkify/thonkify | [
17,
1,
17,
3,
1501859450
] |
def __init__(
self, plotly_name="opacitysrc", parent_name="scattercarpet.marker", **kwargs | plotly/python-api | [
13052,
2308,
13052,
1319,
1385013188
] |
def arrangeWords(self, text: str) -> str:
words = text.split()
table = collections.defaultdict(list)
for word in words:
table[len(word)].append(word)
result = []
for key in sorted(table):
result.extend(table[key])
return ' '.join(result).capitaliz... | jiadaizhao/LeetCode | [
39,
21,
39,
2,
1502171846
] |
def __init__(
self,
plotly_name="familysrc",
parent_name="funnelarea.hoverlabel.font",
**kwargs | plotly/plotly.py | [
13052,
2308,
13052,
1319,
1385013188
] |
def __init__(self, log, *args, **kw):
dv.DataViewCustomRenderer.__init__(self, *args, **kw)
self.log = log
self.value = None | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def GetValue(self):
#self.log.write('MyCustomRenderer.GetValue\n')
return self.value | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def Render(self, rect, dc, state):
if state != 0:
self.log.write('Render: %s, %d\n' % (rect, state))
if not state & dv.DATAVIEW_CELL_SELECTED:
# we'll draw a shaded background to see if the rect correctly
# fills the cell
dc.SetBrush(wx.Brush('light grey'... | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def HasEditorCtrl(self):
self.log.write('HasEditorCtrl')
return True | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def GetValueFromEditorCtrl(self, editor):
self.log.write('GetValueFromEditorCtrl: %s' % editor)
value = editor.GetValue()
return True, value | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def LeftClick(self, pos, cellRect, model, item, col):
self.log.write('LeftClick')
return False | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def __init__(self, parent, log, model=None, data=None):
self.log = log
wx.Panel.__init__(self, parent, -1)
# Create a dataview control
self.dvc = dv.DataViewCtrl(self, style=wx.BORDER_THEME
| dv.DV_ROW_LINES
... | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def main():
from data import musicdata
app = wx.App()
frm = wx.Frame(None, title="CustomRenderer sample", size=(700,500))
pnl = TestPanel(frm, sys.stdout, data=musicdata)
frm.Show()
app.MainLoop() | dnxbjyj/python-basic | [
1,
4,
1,
11,
1501510345
] |
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_at_resource_group_level(
self,
resource_group_name: str,
filter: Optional[str] = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_at_resource_level(
self,
resource_group_name: str,
resource_provider_namespace: str,
parent_resource_path: str,
resource_type: str,
resource_name: str,
filter: Optional[str] = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_at_subscription_level(
self,
filter: Optional[str] = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_by_scope(
self,
scope: str,
filter: Optional[str] = None,
**kwargs: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
if not next_link: | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(self):
self._state = None | tailhook/tilenol | [
60,
11,
60,
7,
1333893143
] |
def update(self):
nval = self._read()
if nval != self._state:
self._state = nval
return True | tailhook/tilenol | [
60,
11,
60,
7,
1333893143
] |
def groups(self):
return self._state | tailhook/tilenol | [
60,
11,
60,
7,
1333893143
] |
def __init__(self, *, filled=False, first_letter=False, right=False):
super().__init__(right=right)
self.filled = filled
self.first_letter = first_letter | tailhook/tilenol | [
60,
11,
60,
7,
1333893143
] |
def check_state(self):
if self.state.dirty:
self.bar.redraw.emit() | tailhook/tilenol | [
60,
11,
60,
7,
1333893143
] |
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def _create_or_update_initial(
self,
resource_group_name, # type: str
ip_groups_name, # type: str
parameters, # type: "_models.IpGroup"
**kwargs # type: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def begin_create_or_update(
self,
resource_group_name, # type: str
ip_groups_name, # type: str
parameters, # type: "_models.IpGroup"
**kwargs # type: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('IpGroup', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def update_groups(
self,
resource_group_name, # type: str
ip_groups_name, # type: str
parameters, # type: "_models.TagsObject"
**kwargs # type: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def _delete_initial(
self,
resource_group_name, # type: str
ip_groups_name, # type: str
**kwargs # type: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def begin_delete(
self,
resource_group_name, # type: str
ip_groups_name, # type: str
**kwargs # type: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {}) | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list_by_resource_group(
self,
resource_group_name, # type: str
**kwargs # type: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list_by_reso... | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
error = self._deserialize.... | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def list(
self,
**kwargs # type: Any | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list.metadat... | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
error = self._deserialize.... | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self, model, inducing_points, variational_distribution, learn_inducing_locations=True, mean_var_batch_dim=None | jrg365/gpytorch | [
3035,
485,
3035,
323,
1497019700
] |
def _expand_inputs(self, x, inducing_points):
# If we haven't explicitly marked a dimension as batch, add the corresponding batch dimension to the input
if self.mean_var_batch_dim is None:
x = x.unsqueeze(-3)
else:
x = x.unsqueeze(self.mean_var_batch_dim - 2)
retu... | jrg365/gpytorch | [
3035,
485,
3035,
323,
1497019700
] |
def is_number(number, topping_list):
"""Will check that what the user enters is really a number and not a letter, also that it is within our list"""
if number in "0123456789":
number = int(number)
if number <= len(topping_list)-1:
return number | frastlin/PyAudioGame | [
5,
4,
5,
2,
1420973210
] |
def add_topping(key):
"""Will add a topping to your pizza"""
number = is_number(key, storage.toppings)
if number or number == 0:
storage.your_toppings.append(storage.toppings[number])
spk("You added %s to your pizza. Your pizza currently has %s on top" % (storage.toppings[number], storage.your_toppings)) | frastlin/PyAudioGame | [
5,
4,
5,
2,
1420973210
] |
def logic(actions):
"""Press a and d to switch from adding and removing toppings, press 0-9 to deal with the toppings and press space to eat the pizza"""
key = actions['key']
if key == "d":
spk("Press a number to remove a topping from your pizza, press a to add toppings again")
storage.screen[0] = "remove"
sto... | frastlin/PyAudioGame | [
5,
4,
5,
2,
1420973210
] |
def prng():
global x
x = math.fmod((x + math.pi) ** 2.0, 1.0)
return x | ActiveState/code | [
1884,
686,
1884,
41,
1500923597
] |
def c(n, k):
if k == 0: return 1
if n == 0: return 0
return c(n - 1, k - 1) + c(n - 1, k) | ActiveState/code | [
1884,
686,
1884,
41,
1500923597
] |
def __init__(self, head, codes):
self._head = '' if head == '' else head + ' '
self._codes = codes | cupy/cupy | [
6731,
672,
6731,
478,
1477994085
] |
def __init__(
self, plotly_name="showexponent", parent_name="parcats.line.colorbar", **kwargs | plotly/python-api | [
13052,
2308,
13052,
1319,
1385013188
] |
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
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