Util [NEW]
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Appends a custom error. This can be used in request or response mapping templates if the template detects an error with the request or with the invocation result. Unlike $util.error(String), the template evaluation will not be interrupted, so that data can be returned to the caller.
Appends a custom error. This can be used in request or response mapping templates if the template detects an error with the request or with the invocation result. Additionally, an errorType can be specified. Unlike $util.error(String, String), the template evaluation will not be interrupted, so that data can be returned to the caller.
Appends a custom error. This can be used in request or response mapping templates if the template detects an error with the request or with the invocation result. Additionally, an errorType and a data field can be specified. Unlike $util.error(String, String, Object), the template evaluation will not be interrupted, so that data can be returned to the caller. The data value will be added to the corresponding error block inside errors in the GraphQL response.
Appends a custom error. This can be used in request or response mapping templates if the template detects an error with the request or with the invocation result. Additionally, an errorType field, a data field, and an errorInfo field can be specified. Unlike $util.error(String, String, Object, Object), the template evaluation will not be interrupted, so that data can be returned to the caller. The data value will be added to the corresponding error block inside errors in the GraphQL response.
Returns true if the specified pattern in the first argument matches the supplied data in the second argument. The pattern must be a regular expression such as $util.matches("a*b", "aaaaab"). The functionality is based on Pattern, which you can reference for further documentation.
Logs the String representation of the provided Objects to the requested log stream when request-level and field-level CloudWatch logging is enabled with log level ALL on an API. This utility will replace all variables indicated by "{}" in the first input format String with the String representation of the provided Objects in order.
Logs the String representation of the provided Objects to the requested log stream when field-level CloudWatch logging is enabled with log level ERROR or log level ALL on an API. This utility will replace all variables indicated by "{}" in the first input format String with the String representation of the provided Objects in order.
$util.dynamodb contains helper methods that make it easier to write and read data to Amazon DynamoDB, such as automatic type mapping and formatting. These methods are designed to make mapping primitive types and Lists to the proper DynamoDB input format automatically, which is a Map of the format { "TYPE" : VALUE }.
In the previous example, we are using the $util.dynamodb.toDynamoDBJson(...) helper to automatically take the generated id and convert it to the DynamoDB representation of a string attribute. We then take all the arguments and convert them to their DynamoDB representations and output them to the attributeValues field in the template.
Each helper has two versions: a version that returns an object (for example, $util.dynamodb.toString(...)), and a version that returns the object as a JSON string (for example, $util.dynamodb.toStringJson(...)). In the previous example, we used the version that returns the data as a JSON string. If you want to manipulate the object before it's used in the template, you can choose to return an object instead, as shown following:
In the previous example, we are returning the converted arguments as a map instead of a JSON string, and are then adding the version and timestamp fields before finally outputting them to the attributeValues field in the template using $util.toJson(...).
This is slightly different to $util.dynamodb.toMap(Map) : Map as it returns only the contents of the DynamoDB attribute value, but not the whole attribute value itself. For example, the following statements are exactly the same:
The rest of this section concerns the case withmap-style datasets. torch.utils.data.Samplerclasses are used to specify the sequence of indices/keys used in data loading.They represent iterable objects over the indices to datasets. E.g., in thecommon case with stochastic gradient decent (SGD), aSampler could randomly permute a list of indicesand yield each one at a time, or yield a small number of them for mini-batchSGD.
torch.utils.data.get_worker_info() returns various useful informationin a worker process (including the worker id, dataset replica, initial seed,etc.), and returns None in main process. Users may use this function indataset code and/or worker_init_fn to individually configure eachdataset replica, and to determine whether the code is running in a workerprocess. For example, this can be particularly helpful in sharding the dataset.
In worker_init_fn, you may access the PyTorch seed set for each workerwith either torch.utils.data.get_worker_info().seedor torch.initial_seed(), and use it to seed other libraries before dataloading.
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