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train | Pruner.forward | Extracts the top-k scoring items with respect to the scorer. We additionally return
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antecedents in a coreference resolut... | allennlp/modules/pruner.py | def forward(self, # pylint: disable=arguments-differ
embeddings: torch.FloatTensor,
mask: torch.LongTensor,
num_items_to_keep: Union[int, torch.LongTensor]) -> Tuple[torch.FloatTensor, torch.LongTensor,
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mask: torch.LongTensor,
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train | add_epoch_number | Add the epoch number to the batch instances as a MetadataField. | allennlp/data/iterators/data_iterator.py | def add_epoch_number(batch: Batch, epoch: int) -> Batch:
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train | DataIterator._take_instances | Take the next `max_instances` instances from the given dataset.
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train | DataIterator._memory_sized_lists | Breaks the dataset into "memory-sized" lists of instances,
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batch_instances : ``Iterable[Instance]``
A candidate batch.
excess : ``Deque[Instance]``
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train | DataIterator.get_num_batches | Returns the number of batches that ``dataset`` will be split into; if you want to track
progress through the batch with the generator produced by ``__call__``, this could be
useful. | allennlp/data/iterators/data_iterator.py | def get_num_batches(self, instances: Iterable[Instance]) -> int:
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Returns the number of batches that ``dataset`` will be split into; if you want to track
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train | DataIterator._create_batches | This method should return one epoch worth of batches. | allennlp/data/iterators/data_iterator.py | def _create_batches(self, instances: Iterable[Instance], shuffle: bool) -> Iterable[Batch]:
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train | replace_cr_with_newline | TQDM and requests use carriage returns to get the training line to update for each batch
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correctly, so we'll just make sure that each batch shows up on its one line.
:param message: the message to permute
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TQDM and requests use carriage returns to get the training line to update for each batch
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... | def replace_cr_with_newline(message: str):
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train | Predictor.capture_model_internals | Context manager that captures the internal-module outputs of
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Context manager that captures the internal-module outputs of
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train | attention | Compute 'Scaled Dot Product Attention | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | def attention(query: torch.Tensor,
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train | subsequent_mask | Mask out subsequent positions. | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | def subsequent_mask(size: int, device: str = 'cpu') -> torch.Tensor:
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mask = torch.tril(torch.ones(size, size, device=device, dtype=torch.int32)).unsqueeze(0)
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mask = torch.tril(torch.ones(size, size, device=device, dtype=torch.int32)).unsqueeze(0)
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train | TransformerEncoder.forward | Pass the input (and mask) through each layer in turn. | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | def forward(self, x, mask):
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train | SublayerConnection.forward | Apply residual connection to any sublayer with the same size. | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | def forward(self, x: torch.Tensor, sublayer: Callable[[torch.Tensor], torch.Tensor]) -> torch.Tensor:
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train | EncoderLayer.forward | Follow Figure 1 (left) for connections. | allennlp/modules/seq2seq_encoders/bidirectional_language_model_transformer.py | def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
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An initaliser which preserves output variance for approximately gaussian
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distribution in the range ``(-sqrt(3/dim[0]) * scale, sqrt(3 / dim[0]) * s... | def uniform_unit_scaling(tensor: torch.Tensor, nonlinearity: str = "linear"):
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train | lstm_hidden_bias | Initialize the biases of the forget gate to 1, and all other gates to 0,
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Initialize the biases of the forget gate to 1, and all other gates to 0,
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train | TableQuestionKnowledgeGraph.read_from_file | We read tables formatted as TSV files here. We assume the first line in the file is a tab
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Nation Olympics Medals
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train | TableQuestionKnowledgeGraph.read_from_json | We read tables formatted as JSON objects (dicts) here. This is useful when you are reading
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train | TableQuestionKnowledgeGraph._get_numbers_from_tokens | Finds numbers in the input tokens and returns them as strings. We do some simple heuristic
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Finds numbers in the input tokens and returns them as strings. We do some simple heuristic
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train | TableQuestionKnowledgeGraph._get_cell_parts | Splits a cell into parts and returns the parts of the cell. We return a list of
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"""
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train | TableQuestionKnowledgeGraph._should_split_column_cells | Returns true if there is any cell in this column that can be split. | allennlp/semparse/contexts/table_question_knowledge_graph.py | def _should_split_column_cells(cls, column_cells: List[str]) -> bool:
"""
Returns true if there is any cell in this column that can be split.
"""
return any(cls._should_split_cell(cell_text) for cell_text in column_cells) | def _should_split_column_cells(cls, column_cells: List[str]) -> bool:
"""
Returns true if there is any cell in this column that can be split.
"""
return any(cls._should_split_cell(cell_text) for cell_text in column_cells) | [
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train | TableQuestionKnowledgeGraph._should_split_cell | Checks whether the cell should be split. We're just doing the same thing that SEMPRE did
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"""
if ', ' in cell_text or '\n' in cell_text or '/' in cell_text:
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entity linking, to provide weak supervision for a learning to search parser. | allennlp/semparse/contexts/table_question_knowledge_graph.py | def get_linked_agenda_items(self) -> List[str]:
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train | main | inp_fn: str, required.
Path to file from which to read Open IE extractions in Open IE4's format.
domain: str, required.
Domain to be used when writing CoNLL format.
out_fn: str, required.
Path to file to which to write the CoNLL format Open IE extractions. | scripts/convert_openie_to_conll.py | def main(inp_fn: str,
domain: str,
out_fn: str) -> None:
"""
inp_fn: str, required.
Path to file from which to read Open IE extractions in Open IE4's format.
domain: str, required.
Domain to be used when writing CoNLL format.
out_fn: str, required.
Path to file to ... | def main(inp_fn: str,
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out_fn: str) -> None:
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Path to file from which to read Open IE extractions in Open IE4's format.
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train | element_from_span | Return an Element from span (list of spacy toks) | scripts/convert_openie_to_conll.py | def element_from_span(span: List[int],
span_type: str) -> Element:
"""
Return an Element from span (list of spacy toks)
"""
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train | split_predicate | Ensure single word predicate
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arguments. | scripts/convert_openie_to_conll.py | def split_predicate(ex: Extraction) -> Extraction:
"""
Ensure single word predicate
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"""
rel_toks = ex.toks[char_to_word_index(ex.rel.span[0], ex.sent) \
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if ... | def split_predicate(ex: Extraction) -> Extraction:
"""
Ensure single word predicate
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"""
rel_toks = ex.toks[char_to_word_index(ex.rel.span[0], ex.sent) \
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train | extraction_to_conll | Return a conll representation of a given input Extraction. | scripts/convert_openie_to_conll.py | def extraction_to_conll(ex: Extraction) -> List[str]:
"""
Return a conll representation of a given input Extraction.
"""
ex = split_predicate(ex)
toks = ex.sent.split(' ')
ret = ['*'] * len(toks)
args = [ex.arg1] + ex.args2
rels_and_args = [("ARG{}".format(arg_ind), arg)
... | def extraction_to_conll(ex: Extraction) -> List[str]:
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Return a conll representation of a given input Extraction.
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train | interpret_span | Return an integer tuple from
textual representation of closed / open spans. | scripts/convert_openie_to_conll.py | def interpret_span(text_spans: str) -> List[int]:
"""
Return an integer tuple from
textual representation of closed / open spans.
"""
m = regex.match("^(?:(?:([\(\[]\d+, \d+[\)\]])|({\d+}))[,]?\s*)+$",
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spans = m.captures(1) + m.captures(2)
int_spans = []
... | def interpret_span(text_spans: str) -> List[int]:
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Return an integer tuple from
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"""
m = regex.match("^(?:(?:([\(\[]\d+, \d+[\)\]])|({\d+}))[,]?\s*)+$",
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spans = m.captures(1) + m.captures(2)
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train | interpret_element | Construct an Element instance from regexp
groups. | scripts/convert_openie_to_conll.py | def interpret_element(element_type: str, text: str, span: str) -> Element:
"""
Construct an Element instance from regexp
groups.
"""
return Element(element_type,
interpret_span(span),
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"""
Construct an Element instance from regexp
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train | parse_element | Parse a raw element into text and indices (integers). | scripts/convert_openie_to_conll.py | def parse_element(raw_element: str) -> List[Element]:
"""
Parse a raw element into text and indices (integers).
"""
elements = [regex.match("^(([a-zA-Z]+)\(([^;]+),List\(([^;]*)\)\))$",
elem.lstrip().rstrip())
for elem
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Parse a raw element into text and indices (integers).
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elements = [regex.match("^(([a-zA-Z]+)\(([^;]+),List\(([^;]*)\)\))$",
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train | convert_sent_to_conll | Given a list of extractions for a single sentence -
convert it to conll representation. | scripts/convert_openie_to_conll.py | def convert_sent_to_conll(sent_ls: List[Extraction]):
"""
Given a list of extractions for a single sentence -
convert it to conll representation.
"""
# Sanity check - make sure all extractions are on the same sentence
assert(len(set([ex.sent for ex in sent_ls])) == 1)
toks = sent_ls[0].sent.... | def convert_sent_to_conll(sent_ls: List[Extraction]):
"""
Given a list of extractions for a single sentence -
convert it to conll representation.
"""
# Sanity check - make sure all extractions are on the same sentence
assert(len(set([ex.sent for ex in sent_ls])) == 1)
toks = sent_ls[0].sent.... | [
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train | pad_line_to_ontonotes | Pad line to conform to ontonotes representation. | scripts/convert_openie_to_conll.py | def pad_line_to_ontonotes(line, domain) -> List[str]:
"""
Pad line to conform to ontonotes representation.
"""
word_ind, word = line[ : 2]
pos = 'XX'
oie_tags = line[2 : ]
line_num = 0
parse = "-"
lemma = "-"
return [domain, line_num, word_ind, word, pos, parse, lemma, '-',\
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"""
Pad line to conform to ontonotes representation.
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word_ind, word = line[ : 2]
pos = 'XX'
oie_tags = line[2 : ]
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train | convert_sent_dict_to_conll | Given a dictionary from sentence -> extractions,
return a corresponding CoNLL representation. | scripts/convert_openie_to_conll.py | def convert_sent_dict_to_conll(sent_dic, domain) -> str:
"""
Given a dictionary from sentence -> extractions,
return a corresponding CoNLL representation.
"""
return '\n\n'.join(['\n'.join(['\t'.join(map(str, pad_line_to_ontonotes(line, domain)))
for line in conver... | def convert_sent_dict_to_conll(sent_dic, domain) -> str:
"""
Given a dictionary from sentence -> extractions,
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"""
return '\n\n'.join(['\n'.join(['\t'.join(map(str, pad_line_to_ontonotes(line, domain)))
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train | deaggregate_record | Given a Kinesis record data that is decoded, deaggregate if it was packed using the
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records that are not aggregated (but will still return them).
decoded_data - the base64 decoded data that comprises either the KPL a... | examples/apps/kinesis-analytics-process-kpl-record/aws_kinesis_agg/deaggregator.py | def deaggregate_record(decoded_data):
'''Given a Kinesis record data that is decoded, deaggregate if it was packed using the
Kinesis Producer Library into individual records. This method will be a no-op for any
records that are not aggregated (but will still return them).
decoded_data - the base64... | def deaggregate_record(decoded_data):
'''Given a Kinesis record data that is decoded, deaggregate if it was packed using the
Kinesis Producer Library into individual records. This method will be a no-op for any
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train | FunctionPolicies._contains_policies | Is there policies data in this resource?
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train | FunctionPolicies._get_type | Returns the type of the given policy
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"""
Returns the type of the given policy
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train | FunctionPolicies._is_policy_template | Is the given policy data a policy template? Policy templates is a dictionary with one key which is the name
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train | Client.get_thing_shadow | r"""
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r"""
Call shadow lambda to obtain current shadow state.
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Call shadow lambda to obtain current shadow state.
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train | Client.update_thing_shadow | r"""
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train | Client.delete_thing_shadow | r"""
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Publishes state information.
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train | Globals.merge | Adds global properties to the resource, if necessary. This method is a no-op if there are no global properties
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Actually perform the merge operation for the given inputs. This method is used as part of the recursion.
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:param global_value: Global value to be merged
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Actually perform the merge operation for the given inputs. This method is used as part of the recursion.
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train | GlobalProperties._merge_dict | Merges the two dictionaries together
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:return: New merged dictionary with values shallow copied | samtranslator/plugins/globals/globals.py | def _merge_dict(self, global_dict, local_dict):
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train | GlobalProperties._token_of | Returns the token type of the input.
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:param input: Input whose type is to be determined
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train | SamTemplateValidator.validate | Is this a valid SAM template dictionary
:param dict template_dict: Data to be validated
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train | generate_car_price | Generates a number within a reasonable range that might be expected for a flight.
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Generates a number within a reasonable range that might be expected for a flight.
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"""
car_types = ['economy', 'standard', 'midsize', 'full size', 'minivan', 'luxury']
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Generates a number within a reasonable range that might be expected for a flight.
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train | generate_hotel_price | Generates a number within a reasonable range that might be expected for a hotel.
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Generates a number within a reasonable range that might be expected for a hotel.
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room_types = ['queen', 'king', 'deluxe']
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train | book_hotel | Performs dialog management and fulfillment for booking a hotel.
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train | book_car | Performs dialog management and fulfillment for booking a car.
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train | dispatch | Called when the user specifies an intent for this bot. | examples/apps/lex-book-trip-python/lambda_function.py | def dispatch(intent_request):
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Called when the user specifies an intent for this bot.
"""
logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))
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train | PullEventSource.to_cloudformation | Returns the Lambda EventSourceMapping to which this pull event corresponds. Adds the appropriate managed
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train | SamParameterValues.add_default_parameter_values | Method to read default values for template parameters and merge with user supplied values.
Example:
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Parameters:
Param1:
Type: String
Default: default_value
Param2:
... | samtranslator/sdk/parameter.py | def add_default_parameter_values(self, sam_template):
"""
Method to read default values for template parameters and merge with user supplied values.
Example:
If the template contains the following parameters defined
Parameters:
Param1:
Type: String
... | def add_default_parameter_values(self, sam_template):
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Method to read default values for template parameters and merge with user supplied values.
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train | SamParameterValues.add_pseudo_parameter_values | Add pseudo parameter values
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"""
Add pseudo parameter values
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"""
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train | DeploymentPreferenceCollection.add | Add this deployment preference to the collection
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:param logical_id: logical id of the resource where this deployment preference applies
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"""
Add this deployment preference to the collection
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... | def add(self, logical_id, deployment_preference_dict):
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train | DeploymentPreferenceCollection.enabled_logical_ids | :return: only the logical id's for the deployment preferences in this collection which are enabled | samtranslator/model/preferences/deployment_preference_collection.py | def enabled_logical_ids(self):
"""
:return: only the logical id's for the deployment preferences in this collection which are enabled
"""
return [logical_id for logical_id, preference in self._resource_preferences.items() if preference.enabled] | def enabled_logical_ids(self):
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/model/preferences/deployment_preference_collection.py#L66-L70 | [
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd |
train | DeploymentPreferenceCollection.deployment_group | :param function_logical_id: logical_id of the function this deployment group belongs to
:return: CodeDeployDeploymentGroup resource | samtranslator/model/preferences/deployment_preference_collection.py | def deployment_group(self, function_logical_id):
"""
:param function_logical_id: logical_id of the function this deployment group belongs to
:return: CodeDeployDeploymentGroup resource
"""
deployment_preference = self.get(function_logical_id)
deployment_group = CodeDeplo... | def deployment_group(self, function_logical_id):
"""
:param function_logical_id: logical_id of the function this deployment group belongs to
:return: CodeDeployDeploymentGroup resource
"""
deployment_preference = self.get(function_logical_id)
deployment_group = CodeDeplo... | [
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/model/preferences/deployment_preference_collection.py#L93-L121 | [
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train | get_welcome_response | If we wanted to initialize the session to have some attributes we could
add those here | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | def get_welcome_response():
""" If we wanted to initialize the session to have some attributes we could
add those here
"""
session_attributes = {}
card_title = "Welcome"
speech_output = "Welcome to the Alexa Skills Kit sample. " \
"Please tell me your favorite color by sayin... | def get_welcome_response():
""" If we wanted to initialize the session to have some attributes we could
add those here
"""
session_attributes = {}
card_title = "Welcome"
speech_output = "Welcome to the Alexa Skills Kit sample. " \
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train | set_color_in_session | Sets the color in the session and prepares the speech to reply to the
user. | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | def set_color_in_session(intent, session):
""" Sets the color in the session and prepares the speech to reply to the
user.
"""
card_title = intent['name']
session_attributes = {}
should_end_session = False
if 'Color' in intent['slots']:
favorite_color = intent['slots']['Color']['va... | def set_color_in_session(intent, session):
""" Sets the color in the session and prepares the speech to reply to the
user.
"""
card_title = intent['name']
session_attributes = {}
should_end_session = False
if 'Color' in intent['slots']:
favorite_color = intent['slots']['Color']['va... | [
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train | on_intent | Called when the user specifies an intent for this skill | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | def on_intent(intent_request, session):
""" Called when the user specifies an intent for this skill """
print("on_intent requestId=" + intent_request['requestId'] +
", sessionId=" + session['sessionId'])
intent = intent_request['intent']
intent_name = intent_request['intent']['name']
# ... | def on_intent(intent_request, session):
""" Called when the user specifies an intent for this skill """
print("on_intent requestId=" + intent_request['requestId'] +
", sessionId=" + session['sessionId'])
intent = intent_request['intent']
intent_name = intent_request['intent']['name']
# ... | [
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py#L148-L167 | [
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train | lambda_handler | Route the incoming request based on type (LaunchRequest, IntentRequest,
etc.) The JSON body of the request is provided in the event parameter. | examples/apps/alexa-skills-kit-color-expert-python/lambda_function.py | def lambda_handler(event, context):
""" Route the incoming request based on type (LaunchRequest, IntentRequest,
etc.) The JSON body of the request is provided in the event parameter.
"""
print("event.session.application.applicationId=" +
event['session']['application']['applicationId'])
"... | def lambda_handler(event, context):
""" Route the incoming request based on type (LaunchRequest, IntentRequest,
etc.) The JSON body of the request is provided in the event parameter.
"""
print("event.session.application.applicationId=" +
event['session']['application']['applicationId'])
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train | LogicalIdGenerator.gen | Generate stable LogicalIds based on the prefix and given data. This method ensures that the logicalId is
deterministic and stable based on input prefix & data object. In other words:
logicalId changes *if and only if* either the `prefix` or `data_obj` changes
Internally we simply use a SHA... | samtranslator/translator/logical_id_generator.py | def gen(self):
"""
Generate stable LogicalIds based on the prefix and given data. This method ensures that the logicalId is
deterministic and stable based on input prefix & data object. In other words:
logicalId changes *if and only if* either the `prefix` or `data_obj` changes
... | def gen(self):
"""
Generate stable LogicalIds based on the prefix and given data. This method ensures that the logicalId is
deterministic and stable based on input prefix & data object. In other words:
logicalId changes *if and only if* either the `prefix` or `data_obj` changes
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] | cccb0c96b5c91e53355ebc07e542467303a5eedd |
train | LogicalIdGenerator.get_hash | Generate and return a hash of data that can be used as suffix of logicalId
:return: Hash of data if it was present
:rtype string | samtranslator/translator/logical_id_generator.py | def get_hash(self, length=HASH_LENGTH):
"""
Generate and return a hash of data that can be used as suffix of logicalId
:return: Hash of data if it was present
:rtype string
"""
data_hash = ""
if not self.data_str:
return data_hash
encoded_da... | def get_hash(self, length=HASH_LENGTH):
"""
Generate and return a hash of data that can be used as suffix of logicalId
:return: Hash of data if it was present
:rtype string
"""
data_hash = ""
if not self.data_str:
return data_hash
encoded_da... | [
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/translator/logical_id_generator.py#L49-L72 | [
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"."... | cccb0c96b5c91e53355ebc07e542467303a5eedd |
train | LogicalIdGenerator._stringify | Stable, platform & language-independent stringification of a data with basic Python type.
We use JSON to dump a string instead of `str()` method in order to be language independent.
:param data: Data to be stringified. If this is one of JSON native types like string, dict, array etc, it will
... | samtranslator/translator/logical_id_generator.py | def _stringify(self, data):
"""
Stable, platform & language-independent stringification of a data with basic Python type.
We use JSON to dump a string instead of `str()` method in order to be language independent.
:param data: Data to be stringified. If this is one of JSON native types... | def _stringify(self, data):
"""
Stable, platform & language-independent stringification of a data with basic Python type.
We use JSON to dump a string instead of `str()` method in order to be language independent.
:param data: Data to be stringified. If this is one of JSON native types... | [
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train | SupportedResourceReferences.add | Add the information that resource with given `logical_id` supports the given `property`, and that a reference
to `logical_id.property` resolves to given `value.
Example:
"MyApi.Deployment" -> "MyApiDeployment1234567890"
:param logical_id: Logical ID of the resource (Ex: MyLambdaF... | samtranslator/intrinsics/resource_refs.py | def add(self, logical_id, property, value):
"""
Add the information that resource with given `logical_id` supports the given `property`, and that a reference
to `logical_id.property` resolves to given `value.
Example:
"MyApi.Deployment" -> "MyApiDeployment1234567890"
... | def add(self, logical_id, property, value):
"""
Add the information that resource with given `logical_id` supports the given `property`, and that a reference
to `logical_id.property` resolves to given `value.
Example:
"MyApi.Deployment" -> "MyApiDeployment1234567890"
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train | SupportedResourceReferences.get | Returns the value of the reference for given logical_id at given property. Ex: MyFunction.Alias
:param logical_id: Logical Id of the resource
:param property: Property of the resource you want to resolve. None if you want to get value of all properties
:return: Value of this property if present... | samtranslator/intrinsics/resource_refs.py | def get(self, logical_id, property):
"""
Returns the value of the reference for given logical_id at given property. Ex: MyFunction.Alias
:param logical_id: Logical Id of the resource
:param property: Property of the resource you want to resolve. None if you want to get value of all prop... | def get(self, logical_id, property):
"""
Returns the value of the reference for given logical_id at given property. Ex: MyFunction.Alias
:param logical_id: Logical Id of the resource
:param property: Property of the resource you want to resolve. None if you want to get value of all prop... | [
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train | encrypt | encrypt leverages KMS encrypt and base64-encode encrypted blob
More info on KMS encrypt API:
https://docs.aws.amazon.com/kms/latest/APIReference/API_encrypt.html | examples/2016-10-31/encryption_proxy/src/encryption.py | def encrypt(key, message):
'''encrypt leverages KMS encrypt and base64-encode encrypted blob
More info on KMS encrypt API:
https://docs.aws.amazon.com/kms/latest/APIReference/API_encrypt.html
'''
try:
ret = kms.encrypt(KeyId=key, Plaintext=message)
encrypted_data = base64.en... | def encrypt(key, message):
'''encrypt leverages KMS encrypt and base64-encode encrypted blob
More info on KMS encrypt API:
https://docs.aws.amazon.com/kms/latest/APIReference/API_encrypt.html
'''
try:
ret = kms.encrypt(KeyId=key, Plaintext=message)
encrypted_data = base64.en... | [
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train | get_tag_list | Transforms the SAM defined Tags into the form CloudFormation is expecting.
SAM Example:
```
...
Tags:
TagKey: TagValue
```
CloudFormation equivalent:
- Key: TagKey
Value: TagValue
```
:param resource_tag_dict: Customer defined dicti... | samtranslator/model/tags/resource_tagging.py | def get_tag_list(resource_tag_dict):
"""
Transforms the SAM defined Tags into the form CloudFormation is expecting.
SAM Example:
```
...
Tags:
TagKey: TagValue
```
CloudFormation equivalent:
- Key: TagKey
Value: TagValue
```
... | def get_tag_list(resource_tag_dict):
"""
Transforms the SAM defined Tags into the form CloudFormation is expecting.
SAM Example:
```
...
Tags:
TagKey: TagValue
```
CloudFormation equivalent:
- Key: TagKey
Value: TagValue
```
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train | ArnGenerator.get_partition_name | Gets the name of the partition given the region name. If region name is not provided, this method will
use Boto3 to get name of the region where this code is running.
This implementation is borrowed from AWS CLI
https://github.com/aws/aws-cli/blob/1.11.139/awscli/customizations/emr/createdefaul... | samtranslator/translator/arn_generator.py | def get_partition_name(cls, region=None):
"""
Gets the name of the partition given the region name. If region name is not provided, this method will
use Boto3 to get name of the region where this code is running.
This implementation is borrowed from AWS CLI
https://github.com/aw... | def get_partition_name(cls, region=None):
"""
Gets the name of the partition given the region name. If region name is not provided, this method will
use Boto3 to get name of the region where this code is running.
This implementation is borrowed from AWS CLI
https://github.com/aw... | [
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"# mechanism, starting from AWS_DEFAULT_REGION environment variable.",
... | cccb0c96b5c91e53355ebc07e542467303a5eedd |
train | DefaultDefinitionBodyPlugin.on_before_transform_template | Hook method that gets called before the SAM template is processed.
The template has passed the validation and is guaranteed to contain a non-empty "Resources" section.
:param dict template_dict: Dictionary of the SAM template
:return: Nothing | samtranslator/plugins/api/default_definition_body_plugin.py | def on_before_transform_template(self, template_dict):
"""
Hook method that gets called before the SAM template is processed.
The template has passed the validation and is guaranteed to contain a non-empty "Resources" section.
:param dict template_dict: Dictionary of the SAM template
... | def on_before_transform_template(self, template_dict):
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Hook method that gets called before the SAM template is processed.
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train | SwaggerEditor.has_path | Returns True if this Swagger has the given path and optional method
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train | SwaggerEditor.method_has_integration | Returns true if the given method contains a valid method definition.
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:return: true if method has one or multiple integrations | samtranslator/swagger/swagger.py | def method_has_integration(self, method):
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train | SwaggerEditor.get_method_contents | Returns the swagger contents of the given method. This checks to see if a conditional block
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train | SwaggerEditor.has_integration | Checks if an API Gateway integration is already present at the given path/method
:param string path: Path name
:param string method: HTTP method
:return: True, if an API Gateway integration is already present | samtranslator/swagger/swagger.py | def has_integration(self, path, method):
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Checks if an API Gateway integration is already present at the given path/method
:param string path: Path name
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:return: True, if an API Gateway integration is already present
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Checks if an API Gateway integration is already present at the given path/method
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train | SwaggerEditor.add_path | Adds the path/method combination to the Swagger, if not already present
:param string path: Path name
:param string method: HTTP method
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Adds the path/method combination to the Swagger, if not already present
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Adds the path/method combination to the Swagger, if not already present
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train | SwaggerEditor.add_lambda_integration | Adds aws_proxy APIGW integration to the given path+method.
:param string path: Path name
:param string method: HTTP Method
:param string integration_uri: URI for the integration. | samtranslator/swagger/swagger.py | def add_lambda_integration(self, path, method, integration_uri,
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Adds aws_proxy APIGW integration to the given path+method.
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train | SwaggerEditor.make_path_conditional | Wrap entire API path definition in a CloudFormation if condition. | samtranslator/swagger/swagger.py | def make_path_conditional(self, path, condition):
"""
Wrap entire API path definition in a CloudFormation if condition.
"""
self.paths[path] = make_conditional(condition, self.paths[path]) | def make_path_conditional(self, path, condition):
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train | SwaggerEditor.add_cors | Add CORS configuration to this path. Specifically, we will add a OPTIONS response config to the Swagger that
will return headers required for CORS. Since SAM uses aws_proxy integration, we cannot inject the headers
into the actual response returned from Lambda function. This is something customers have ... | samtranslator/swagger/swagger.py | def add_cors(self, path, allowed_origins, allowed_headers=None, allowed_methods=None, max_age=None,
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"""
Add CORS configuration to this path. Specifically, we will add a OPTIONS response config to the Swagger that
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train | SwaggerEditor._options_method_response_for_cors | Returns a Swagger snippet containing configuration for OPTIONS HTTP Method to configure CORS.
This snippet is taken from public documentation:
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"""
Returns a Swagger snippet containing configuration for OPTIONS HTTP Method to configure CORS.
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train | SwaggerEditor._make_cors_allowed_methods_for_path | Creates the value for Access-Control-Allow-Methods header for given path. All HTTP methods defined for this
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train | SwaggerEditor.add_authorizers | Add Authorizer definitions to the securityDefinitions part of Swagger.
:param list authorizers: List of Authorizer configurations which get translated to securityDefinitions. | samtranslator/swagger/swagger.py | def add_authorizers(self, authorizers):
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Add Authorizer definitions to the securityDefinitions part of Swagger.
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train | SwaggerEditor.set_path_default_authorizer | Sets the DefaultAuthorizer for each method on this path. The DefaultAuthorizer won't be set if an Authorizer
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:param string path: Path name
:param string default_authorizer: Name of the authorizer to use as the default. Must be a key in the
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train | SwaggerEditor.add_auth_to_method | Adds auth settings for this path/method. Auth settings currently consist solely of Authorizers
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Adds auth settings for this path/method. Auth settings currently consist solely of Authorizers
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"A... | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/swagger/swagger.py#L411-L429 | [
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train | SwaggerEditor.add_gateway_responses | Add Gateway Response definitions to Swagger.
:param dict gateway_responses: Dictionary of GatewayResponse configuration which gets translated. | samtranslator/swagger/swagger.py | def add_gateway_responses(self, gateway_responses):
"""
Add Gateway Response definitions to Swagger.
:param dict gateway_responses: Dictionary of GatewayResponse configuration which gets translated.
"""
self.gateway_responses = self.gateway_responses or {}
for response_... | def add_gateway_responses(self, gateway_responses):
"""
Add Gateway Response definitions to Swagger.
:param dict gateway_responses: Dictionary of GatewayResponse configuration which gets translated.
"""
self.gateway_responses = self.gateway_responses or {}
for response_... | [
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/swagger/swagger.py#L516-L525 | [
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... | cccb0c96b5c91e53355ebc07e542467303a5eedd |
train | SwaggerEditor.swagger | Returns a **copy** of the Swagger document as a dictionary.
:return dict: Dictionary containing the Swagger document | samtranslator/swagger/swagger.py | def swagger(self):
"""
Returns a **copy** of the Swagger document as a dictionary.
:return dict: Dictionary containing the Swagger document
"""
# Make sure any changes to the paths are reflected back in output
self._doc["paths"] = self.paths
if self.security_de... | def swagger(self):
"""
Returns a **copy** of the Swagger document as a dictionary.
:return dict: Dictionary containing the Swagger document
"""
# Make sure any changes to the paths are reflected back in output
self._doc["paths"] = self.paths
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/swagger/swagger.py#L528-L543 | [
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train | SwaggerEditor.is_valid | Checks if the input data is a Swagger document
:param dict data: Data to be validated
:return: True, if data is a Swagger | samtranslator/swagger/swagger.py | def is_valid(data):
"""
Checks if the input data is a Swagger document
:param dict data: Data to be validated
:return: True, if data is a Swagger
"""
return bool(data) and \
isinstance(data, dict) and \
bool(data.get("swagger")) and \
... | def is_valid(data):
"""
Checks if the input data is a Swagger document
:param dict data: Data to be validated
:return: True, if data is a Swagger
"""
return bool(data) and \
isinstance(data, dict) and \
bool(data.get("swagger")) and \
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/swagger/swagger.py#L546-L556 | [
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"(... | cccb0c96b5c91e53355ebc07e542467303a5eedd |
train | SwaggerEditor._normalize_method_name | Returns a lower case, normalized version of HTTP Method. It also know how to handle API Gateway specific methods
like "ANY"
NOTE: Always normalize before using the `method` value passed in as input
:param string method: Name of the HTTP Method
:return string: Normalized method name | samtranslator/swagger/swagger.py | def _normalize_method_name(method):
"""
Returns a lower case, normalized version of HTTP Method. It also know how to handle API Gateway specific methods
like "ANY"
NOTE: Always normalize before using the `method` value passed in as input
:param string method: Name of the HTTP M... | def _normalize_method_name(method):
"""
Returns a lower case, normalized version of HTTP Method. It also know how to handle API Gateway specific methods
like "ANY"
NOTE: Always normalize before using the `method` value passed in as input
:param string method: Name of the HTTP M... | [
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] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/swagger/swagger.py#L576-L593 | [
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... | cccb0c96b5c91e53355ebc07e542467303a5eedd |
train | GlobalsPlugin.on_before_transform_template | Hook method that runs before a template gets transformed. In this method, we parse and process Globals section
from the template (if present).
:param dict template_dict: SAM template as a dictionary | samtranslator/plugins/globals/globals_plugin.py | def on_before_transform_template(self, template_dict):
"""
Hook method that runs before a template gets transformed. In this method, we parse and process Globals section
from the template (if present).
:param dict template_dict: SAM template as a dictionary
"""
try:
... | def on_before_transform_template(self, template_dict):
"""
Hook method that runs before a template gets transformed. In this method, we parse and process Globals section
from the template (if present).
:param dict template_dict: SAM template as a dictionary
"""
try:
... | [
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"."
] | awslabs/serverless-application-model | python | https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/plugins/globals/globals_plugin.py#L20-L40 | [
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"(",
"[",
"... | cccb0c96b5c91e53355ebc07e542467303a5eedd |
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