function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def testRandom(self):
# Random shapes of rank 4, random indices
for _ in range(5):
shape = np.random.randint(1, 20, size=4)
indices = np.random.randint(shape[0], size=2 * shape[0])
self._TestCase(_AsLong(list(shape)), list(indices)) | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testSubRandom(self):
# Random shapes of rank 4, random indices
for _ in range(5):
shape = np.random.randint(1, 20, size=4)
indices = np.random.randint(shape[0], size=2 * shape[0])
self._TestCase(_AsLong(list(shape)), list(indices), state_ops.scatter_sub) | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testWrongShape(self):
# Indices and values mismatch.
var = variables.Variable(
array_ops.zeros(shape=[1024, 64, 64], dtype=dtypes.float32))
indices = array_ops.placeholder(dtypes.int32, shape=[32])
values = array_ops.placeholder(dtypes.float32, shape=[33, 64, 64])
with self.assertRaises(... | tensorflow/tensorflow | [
171949,
87931,
171949,
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1446859160
] |
def _EmbeddingParams(num_shards,
vocab_size,
dtype=dtypes.float32,
shape=None,
use_shapeless_placeholder=False):
p = []
params = {}
feed_dict = {}
if not shape:
shape = [10]
for i in range(num_shards):
shard_shape = [v... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def _EmbeddingResult(params,
id_vals,
num_shards,
vocab_size,
partition_strategy="mod",
weight_vals=None):
if weight_vals is None:
weight_vals = np.copy(id_vals)
weight_vals.fill(1)
values = []
weights... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testSimpleSharded(self):
with self.cached_session():
num_shards = 2
vocab_size = 4
p, params, feed_dict = _EmbeddingParams(num_shards, vocab_size)
id_vals = np.array([0, 0])
ids = constant_op.constant(list(id_vals), dtype=dtypes.int32)
print("Construct ids", ids.get_shape())... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testMaxNorm(self):
with self.cached_session():
embeddings = constant_op.constant([[2.0]])
ids = constant_op.constant([0], dtype=dtypes.int32)
embedding = embedding_ops.embedding_lookup(
[embeddings], ids, max_norm=1.0)
self.assertAllEqual(embedding, [[1.0]]) | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testMaxNormNontrivial(self):
with self.cached_session():
embeddings = constant_op.constant([[2.0, 4.0], [3.0, 1.0]])
ids = constant_op.constant([0, 1], dtype=dtypes.int32)
embedding = embedding_ops.embedding_lookup(
[embeddings], ids, max_norm=2.0)
norms = math_ops.sqrt(
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testSimpleShardedPartitionedVariable(self):
with self.cached_session() as sess:
num_shards = 2
vocab_size = 4
p, p_variable, params, feed_dict = _EmbeddingParamsAsPartitionedVariable(
num_shards, vocab_size)
id_vals = np.array([0, 0])
ids = constant_op.constant(list(id_v... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testSimpleShardedPartitionedResourceVariable(self):
with self.cached_session() as sess:
num_shards = 2
vocab_size = 4
p, p_variable, params, _ = _EmbeddingParamsAsPartitionedVariable(
num_shards, vocab_size, use_resource=True)
id_vals = np.array([0, 0])
ids = constant_op... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testShardedModPartitioningInt32Ids(self):
with self.cached_session():
num_shards = 5
vocab_size = 13
# Embedding dimensions is 10. The vocab_size x 10 embedding
# parameters are spread in num_shards matrices, so the first
# 3 shards are 3 x 10 and the last 2 shards are 2 x 10.
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testShardedModPartitioningInt64Ids(self):
with self.cached_session():
num_shards = 5
vocab_size = 13
# Embedding dimensions is 10. The vocab_size x 10 embedding
# parameters are spread in num_shards matrices, so the first
# 3 shards are 3 x 10 and the last 2 shards are 2 x 10.
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testShardedDivPartitioningInt32Ids(self):
with self.cached_session():
num_shards = 5
vocab_size = 13
# Embedding dimensions is 10. The vocab_size x 10 embedding
# parameters are spread in num_shards matrices, so the first
# 3 shards are 3 x 10 and the last 2 shards are 2 x 10.
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testShardedDivPartitioningInt32IdsPartitionedVariable(self):
with self.cached_session():
num_shards = 5
vocab_size = 13
# Embedding dimensions is 10. The vocab_size x 10 embedding
# parameters are spread in num_shards matrices, so the first
# 3 shards are 3 x 10 and the last 2 shar... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testShardedDivPartitioningInt64Ids(self):
with self.cached_session():
num_shards = 5
vocab_size = 13
# Embedding dimensions is 10. The vocab_size x 10 embedding
# parameters are spread in num_shards matrices, so the first
# 3 shards are 3 x 10 and the last 2 shards are 2 x 10.
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testShardedDivPartitioningUnknownParamShape(self):
with self.cached_session():
num_shards = 5
vocab_size = 13
# Embedding dimensions is 10. The vocab_size x 10 embedding
# parameters are spread in num_shards matrices, so the first
# 3 shards are 3 x 10 and the last 2 shards are 2 x... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testGradientsEmbeddingLookup(self):
vocab_size = 9
num_ids = 10
id_vals = list(np.random.randint(vocab_size, size=num_ids))
tf_logging.vlog(1, id_vals)
for ids_shape in [(10,), (2, 5)]:
for num_shards in [1, 3]:
with self.cached_session():
ids = constant_op.constant(
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testGradientsEmbeddingLookupWithComputedParams(self):
vocab_size = 9
num_ids = 5
id_vals = list(np.random.randint(vocab_size, size=num_ids))
tf_logging.vlog(1, id_vals)
for num_shards in [1, 3]:
with self.cached_session():
ids = constant_op.constant(id_vals, dtype=dtypes.int32)
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testConstructionSharded(self):
with ops.Graph().as_default():
p = []
for _ in range(2):
p += [
variables.Variable(
array_ops.zeros(shape=[100, 100], dtype=dtypes.float32))
]
ids = constant_op.constant([0, 1, 1, 17], dtype=dtypes.int32)
embedd... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testHigherRank(self):
np.random.seed(8)
with self.cached_session():
for params_shape in (12,), (6, 3):
params = np.random.randn(*params_shape)
for ids_shape in (3, 2), (4, 3):
ids = np.random.randint(
params.shape[0], size=np.prod(ids_shape)).reshape(ids_shape)
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testHigherRankMaxNorm(self):
np.random.seed(8)
with self.cached_session():
for params_shape in (12,), (6, 3), (6, 2, 3):
# Test embedding rank 0, 1, 2.
# Note: the first dimension must be a common multiple of procs below.
params = 2 * np.ones(params_shape)
params_norm =... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testTransform(self):
# This tests all combinations of:
# - ids rank 0, 1, >1
# - params sharded/unsharded
# It always applies max_norm.
np.random.seed(8)
l2_norm = 2.
with self.cached_session():
# Param values are in [l2_norm, l2_norm+1) so it will always clip.
params = n... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def _RandomIdsAndWeights(self, batch_size, vocab_size):
max_val_per_entry = 6
vals_per_batch_entry = np.random.randint(
1, max_val_per_entry, size=batch_size)
num_vals = np.sum(vals_per_batch_entry)
ids = np.random.randint(vocab_size, size=num_vals)
weights = 1 + np.random.rand(num_vals)
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testEmbeddingLookupSparse(self):
vocab_size = 13
batch_size = 10
param_shape = [2, 5]
expected_lookup_result_shape = [None] + param_shape
sp_ids, sp_weights, ids, weights, vals_per_batch_entry = (
self._RandomIdsAndWeights(batch_size, vocab_size))
grouped_ids = self._GroupByBatchEn... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testGradientsEmbeddingLookupSparse(self):
vocab_size = 12
batch_size = 4
param_shape = [2, 3]
sp_ids, sp_weights, _, _, _ = (self._RandomIdsAndWeights(
batch_size, vocab_size))
for num_shards, combiner, dtype, ignore_weights in itertools.product(
[1, 3], ["sum", "mean", "sqrtn"]... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testIncompatibleShapes(self):
with self.cached_session():
x, _, _ = _EmbeddingParams(1, 10, dtype=dtypes.float32)
sp_ids = sparse_tensor.SparseTensor(
constant_op.constant([[0, 0], [0, 1], [1, 0]], dtypes.int64),
constant_op.constant([0, 1, 2], dtypes.int32),
constant_o... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def _random_weights(self, vocab_size=4, embed_dim=4, num_shards=1):
assert vocab_size > 0
assert embed_dim > 0
assert num_shards > 0
assert num_shards <= vocab_size
initializer = init_ops.truncated_normal_initializer(
mean=0.0, stddev=1.0 / math.sqrt(vocab_size), dtype=dtypes.float32)
e... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def _ids_and_weights_3d(self):
# Each (2-D) index demonstrates a test case:
# Index 0, 0: multiple valid ids, 1 invalid id, weighted mean
# Index 0, 1: all ids are invalid (leaving no valid ids after pruning)
# Index 0, 2: no ids to begin with
# Index 1, 0: single id
# Index 1, 1: all ... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_return_zero_vector(self):
with self.cached_session():
embedding_weights = self._random_weights()
sparse_ids, sparse_weights = self._ids_and_weights_2d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(embedding_weights,
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_return_special_vector(self):
with self.cached_session():
embedding_weights = self._random_weights()
sparse_ids, sparse_weights = self._ids_and_weights_2d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(
embe... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_no_weights(self):
with self.cached_session():
embedding_weights = self._random_weights()
sparse_ids, _ = self._ids_and_weights_2d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(embedding_weights,
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_partitioned(self):
with self.cached_session():
embedding_weights = self._random_weights(num_shards=3)
sparse_ids, _ = self._ids_and_weights_2d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(embedding_weights,
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_partitioned_inconsistent_weights(self):
with self.cached_session():
embedding_weights = self._random_weights(num_shards=3)
sparse_ids, sparse_weights = self._ids_and_weights_2d()
embedding_weights[1] = embedding_weights[1].astype(np.float64)
self.assert... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_3d_return_zero_vector(self):
with self.cached_session():
embedding_weights = self._random_weights()
sparse_ids, sparse_weights = self._ids_and_weights_3d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(embedding_weights,
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_3d_return_special_vector(self):
with self.cached_session():
embedding_weights = self._random_weights()
sparse_ids, sparse_weights = self._ids_and_weights_3d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(
e... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_3d_no_weights(self):
with self.cached_session():
embedding_weights = self._random_weights()
sparse_ids, _ = self._ids_and_weights_3d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(embedding_weights,
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_3d_partitioned(self):
with self.cached_session():
embedding_weights = self._random_weights(num_shards=3)
sparse_ids, _ = self._ids_and_weights_3d()
embedding_lookup_result = (
embedding_ops.safe_embedding_lookup_sparse_v2(embedding_weights,
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def test_safe_embedding_lookup_sparse_3d_partitioned_inconsistent_weights(
self):
with self.cached_session():
embedding_weights = self._random_weights(num_shards=3)
sparse_ids, sparse_weights = self._ids_and_weights_3d()
embedding_weights[1] = embedding_weights[1].astype(np.float64)
s... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testCint32Cpu(self):
with self.session(use_gpu=False):
indices = [
ops.convert_to_tensor([0, 1, 2]),
ops.convert_to_tensor([2, 3])
]
values = [
ops.convert_to_tensor([12, 23, 34]),
ops.convert_to_tensor([1, 2])
]
self.assertAllEqual(
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testCint32Gpu(self):
with self.session():
indices = [
ops.convert_to_tensor([0, 1, 2]),
ops.convert_to_tensor([2, 3])
]
values = [
ops.convert_to_tensor([12, 23, 34]),
ops.convert_to_tensor([1, 2])
]
self.assertAllEqual(
data_flow_o... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testInt32Cpu(self):
with self.session(use_gpu=False):
indices = [
ops.convert_to_tensor([0, 1, 2]),
ops.convert_to_tensor([2, 3])
]
values = [
ops.convert_to_tensor([12, 23, 34]),
ops.convert_to_tensor([1, 2])
]
self.assertAllEqual(
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testInt32Gpu(self):
with self.session():
indices = [
ops.convert_to_tensor([0, 1, 2]),
ops.convert_to_tensor([2, 3])
]
values = [
ops.convert_to_tensor([12, 23, 34]),
ops.convert_to_tensor([1, 2])
]
self.assertAllEqual(
data_flow_op... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testSumGradArgs(self):
with self.session(use_gpu=False):
indices = [
ops.convert_to_tensor([0, 1, 2, 3]),
ops.convert_to_tensor([2, 3])
]
values = [
ops.convert_to_tensor([2, 3, 5, 7]),
ops.convert_to_tensor([1, 1])
]
self.assertAllEqual(
... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testStitchOrder(self):
with self.cached_session():
indices = []
np_values = []
values = []
for _ in range(10):
indices.extend([ops.convert_to_tensor(np.arange(100).astype(np.int32))])
np_values.extend([np.random.uniform(size=100)])
values.extend([ops.convert_to_te... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testCint32Cpu(self):
with self.session(use_gpu=False):
indices = [
ops.convert_to_tensor([0, 1, 4, 6]),
ops.convert_to_tensor([2, 3, 5])
]
values = [
ops.convert_to_tensor([12, 23, 34, 45]),
ops.convert_to_tensor([1, 2, 3])
]
self.assertAllEq... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testInt32Cpu(self):
with self.session(use_gpu=False):
indices = [
ops.convert_to_tensor([0, 1, 5, 6, 7]),
ops.convert_to_tensor([2, 4, 3])
]
values = [
ops.convert_to_tensor([12, 23, 34, 45, 56]),
ops.convert_to_tensor([1, 3, 2])
]
self.asser... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def testSimple(self):
with self.session(use_gpu=False):
indices = [ops.convert_to_tensor([0, 1]), ops.convert_to_tensor([2, 3])]
values = [ops.convert_to_tensor([2, 3]), ops.convert_to_tensor([1, 1])]
self.assertAllEqual(
data_flow_ops.parallel_dynamic_stitch(indices, values), [2, 3, 1, ... | tensorflow/tensorflow | [
171949,
87931,
171949,
2300,
1446859160
] |
def ahead_by(self):
self._completeIfNotSet(self._ahead_by)
return self._NoneIfNotSet(self._ahead_by) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def base_commit(self):
self._completeIfNotSet(self._base_commit)
return self._NoneIfNotSet(self._base_commit) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def behind_by(self):
self._completeIfNotSet(self._behind_by)
return self._NoneIfNotSet(self._behind_by) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def commits(self):
self._completeIfNotSet(self._commits)
return self._NoneIfNotSet(self._commits) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def diff_url(self):
self._completeIfNotSet(self._diff_url)
return self._NoneIfNotSet(self._diff_url) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def files(self):
self._completeIfNotSet(self._files)
return self._NoneIfNotSet(self._files) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def html_url(self):
self._completeIfNotSet(self._html_url)
return self._NoneIfNotSet(self._html_url) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def patch_url(self):
self._completeIfNotSet(self._patch_url)
return self._NoneIfNotSet(self._patch_url) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def permalink_url(self):
self._completeIfNotSet(self._permalink_url)
return self._NoneIfNotSet(self._permalink_url) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def status(self):
self._completeIfNotSet(self._status)
return self._NoneIfNotSet(self._status) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def total_commits(self):
self._completeIfNotSet(self._total_commits)
return self._NoneIfNotSet(self._total_commits) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def url(self):
self._completeIfNotSet(self._url)
return self._NoneIfNotSet(self._url) | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def __init__(self,
logits=None,
probs=None,
validate_args=True,
allow_nan_stats=False,
name="Geometric"):
"""Construct Geometric distributions.
Args:
logits: Floating-point `Tensor` with shape `[B1, ..., Bb]` where `b >= 0`
... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def logits(self):
"""Log-odds of a `1` outcome (vs `0`)."""
return self._logits | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def probs(self):
"""Probability of a `1` outcome (vs `0`)."""
return self._probs | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _batch_shape(self):
return self.probs.get_shape() | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _event_shape(self):
return tensor_shape.scalar() | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _cdf(self, counts):
if self.validate_args:
# We set `check_integer=False` since the CDF is defined on whole real
# line.
counts = math_ops.floor(
distribution_util.embed_check_nonnegative_discrete(
counts, check_integer=False))
counts *= array_ops.ones_like(self.pro... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _entropy(self):
probs = self._probs
if self.validate_args:
probs = control_flow_ops.with_dependencies(
[check_ops.assert_less(
probs,
constant_op.constant(1., probs.dtype),
message="Entropy is undefined when logits = inf or probs = 1.")],
p... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _variance(self):
return self._mean() / self.probs | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def __init__(self, context, query, image=None, fanart=None):
if image is None:
image = context.create_resource_path('media/search.png')
pass
DirectoryItem.__init__(self, query, context.create_uri([constants.paths.SEARCH, 'query'], {'q': query}),
im... | azumimuo/family-xbmc-addon | [
1,
3,
1,
2,
1456692116
] |
def maxNumEdgesToRemove(self, n: int, edges: List[List[int]]) -> int:
parent = list(range(n + 1))
def findParent(i):
while parent[i] != i:
parent[i] = parent[parent[i]]
i = parent[i]
return i | jiadaizhao/LeetCode | [
39,
21,
39,
2,
1502171846
] |
def union(u, v):
pu = findParent(u)
pv = findParent(v)
if pu != pv:
parent[pv] = pu
return 1
else:
return 0 | jiadaizhao/LeetCode | [
39,
21,
39,
2,
1502171846
] |
def __str__(self):
return self.name | IQSS/miniverse | [
2,
7,
2,
26,
1467318581
] |
def save(self, *args, **kwargs):
self.slug = slugify(self.name)
super(Installation, self).save(*args, **kwargs) | IQSS/miniverse | [
2,
7,
2,
26,
1467318581
] |
def view_marker(self):
#return self.logo.url
if self.marker:
im = '<img src="%s" />' % (self.marker.url)
return im
return 'n/a' | IQSS/miniverse | [
2,
7,
2,
26,
1467318581
] |
def view_logo(self, force_width=None):
#return self.logo.url
if self.logo:
if force_width:
im = ('<img src="{0}" width="{1}"/ >'
'<br />(width forced to {1}px)').format(\
self.logo.url, force_width)
return im
... | IQSS/miniverse | [
2,
7,
2,
26,
1467318581
] |
def to_json(self, as_string=False, pretty=False):
"""Returns an OrderedDict of the installation attributes"""
od = OrderedDict()
od['id'] = self.id
od['name'] = self.name
od['full_name'] = self.full_name
od['is_active'] = self.is_active
od['description'] = self.d... | IQSS/miniverse | [
2,
7,
2,
26,
1467318581
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
def __init__(
self,
**kwargs | Azure/azure-sdk-for-python | [
3526,
2256,
3526,
986,
1335285972
] |
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