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export_dictionary_entries

export_dictionary_entries(
    id: DictionaryId,
    *,
    config_overrides: Optional[ElementalInferenceClientConfig] = None,
) -> ExportDictionaryEntriesResponse

Exports the entries from the specified dictionary.

Parameters:

  • id (DictionaryId) –

    The ID of the dictionary whose entries you want to export.

Raises:

  • AccessDeniedException –

    You do not have sufficient access to perform this action.

  • InternalServerErrorException –

    An internal server error occurred. This is a temporary condition and the request can be retried. If the problem persists, contact AWS Support.

  • ResourceNotFoundException –

    The resource specified in the action doesn't exist.

  • TooManyRequestException –

    The request was denied due to request throttling. Too many requests have been made within a given time period. Reduce the frequency of requests and use exponential backoff when retrying.

  • ValidationException –

    The input fails to satisfy the constraints specified by the service. Check the error message for details about which parameter or field is invalid and correct the request before retrying.

  • UnknownServiceError –

    The service returned an error code this client does not model.

Source code in docs_src/capo_elementalinference/_services/elemental_inference.py
def export_dictionary_entries(
    self,
    id: "capo_elementalinference.types.dictionary_id.DictionaryId",
    *,
    config_overrides: Optional[ElementalInferenceClientConfig] = None,
) -> "capo_elementalinference.types.export_dictionary_entries_response.ExportDictionaryEntriesResponse":
    """<p>Exports the entries from the specified dictionary.</p>

    Args:
        id: <p>The ID of the dictionary whose entries you want to export.</p>

    Raises:
        capo_elementalinference.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_elementalinference.errors.internal_server_error_exception.InternalServerErrorException: <p>An internal server error occurred. This is a temporary condition and the request can be retried. If the problem persists, contact AWS Support. </p>
        capo_elementalinference.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource specified in the action doesn't exist.</p>
        capo_elementalinference.errors.too_many_request_exception.TooManyRequestException: <p>The request was denied due to request throttling. Too many requests have been made within a given time period. Reduce the frequency of requests and use exponential backoff when retrying. </p>
        capo_elementalinference.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by the service. Check the error message for details about which parameter or field is invalid and correct the request before retrying. </p>
        capo_elementalinference.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_elementalinference.types.export_dictionary_entries_request.ExportDictionaryEntriesRequest]",
    ) -> OperationResponse[
        "capo_elementalinference.types.export_dictionary_entries_response.ExportDictionaryEntriesResponse"
    ]:
        import capo_elementalinference._operations.elemental_inference.export_dictionary_entries

        output, http_response = (
            capo_elementalinference._operations.elemental_inference.export_dictionary_entries.export_dictionary_entries(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ExportDictionaryEntries")
    )
    input_: capo_elementalinference.types.export_dictionary_entries_request.ExportDictionaryEntriesRequest = {
        "id": id
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output