forked from lthn/blockchain
Introduces a new Python client SDK for the lthn API, generated via OpenAPI Generator. Includes source code, models, API classes, documentation, tests, CI/CD workflows for GitHub and GitLab, and project configuration files.
2.4 KiB
Generated
2.4 KiB
Generated
TxProcessingPerformanceModel
Properties
| Name | Type | Description | Notes |
|---|---|---|---|
| tx_check_inputs | int | [optional] | |
| tx_add_one_tx | int | [optional] | |
| tx_process_extra | int | [optional] | |
| tx_process_attachment | int | [optional] | |
| tx_process_inputs | int | [optional] | |
| tx_push_global_index | int | [optional] | |
| tx_check_exist | int | [optional] | |
| tx_print_log | int | [optional] | |
| tx_prapare_append | int | [optional] | |
| tx_append | int | [optional] | |
| tx_append_rl_wait | int | [optional] | |
| tx_append_is_expired | int | [optional] | |
| tx_store_db | int | [optional] | |
| tx_check_inputs_prefix_hash | int | [optional] | |
| tx_check_inputs_attachment_check | int | [optional] | |
| tx_check_inputs_loop | int | [optional] | |
| tx_check_inputs_loop_kimage_check | int | [optional] | |
| tx_check_inputs_loop_ch_in_val_sig | int | [optional] | |
| tx_check_inputs_loop_scan_outputkeys_get_item_size | int | [optional] | |
| tx_check_inputs_loop_scan_outputkeys_relative_to_absolute | int | [optional] | |
| tx_check_inputs_loop_scan_outputkeys_loop | int | [optional] | |
| tx_check_inputs_loop_scan_outputkeys_loop_get_subitem | int | [optional] | |
| tx_check_inputs_loop_scan_outputkeys_loop_find_tx | int | [optional] | |
| tx_check_inputs_loop_scan_outputkeys_loop_handle_output | int | [optional] | |
| tx_mixin_count | int | [optional] |
Example
from lthn.models.tx_processing_performance_model import TxProcessingPerformanceModel
# TODO update the JSON string below
json = "{}"
# create an instance of TxProcessingPerformanceModel from a JSON string
tx_processing_performance_model_instance = TxProcessingPerformanceModel.from_json(json)
# print the JSON string representation of the object
print(TxProcessingPerformanceModel.to_json())
# convert the object into a dict
tx_processing_performance_model_dict = tx_processing_performance_model_instance.to_dict()
# create an instance of TxProcessingPerformanceModel from a dict
tx_processing_performance_model_from_dict = TxProcessingPerformanceModel.from_dict(tx_processing_performance_model_dict)