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# coding: utf-8 # Copyright (c) 2016, 2026, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. # NOTE: This class is auto generated by OracleSDKGenerator. DO NOT EDIT. API Version: 20221109 from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class DatasetSummary(object): """ Summary of count of samples used during model training. """ def __init__(self, **kwargs): """ Initializes a new DatasetSummary object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param training_sample_count: The value to assign to the training_sample_count property of this DatasetSummary. :type training_sample_count: int :param validation_sample_count: The value to assign to the validation_sample_count property of this DatasetSummary. :type validation_sample_count: int :param test_sample_count: The value to assign to the test_sample_count property of this DatasetSummary. :type test_sample_count: int """ self.swagger_types = { 'training_sample_count': 'int', 'validation_sample_count': 'int', 'test_sample_count': 'int' } self.attribute_map = { 'training_sample_count': 'trainingSampleCount', 'validation_sample_count': 'validationSampleCount', 'test_sample_count': 'testSampleCount' } self._training_sample_count = None self._validation_sample_count = None self._test_sample_count = None @property def training_sample_count(self): """ Gets the training_sample_count of this DatasetSummary. Number of samples used for training the model. :return: The training_sample_count of this DatasetSummary. :rtype: int """ return self._training_sample_count @training_sample_count.setter def training_sample_count(self, training_sample_count): """ Sets the training_sample_count of this DatasetSummary. Number of samples used for training the model. :param training_sample_count: The training_sample_count of this DatasetSummary. :type: int """ self._training_sample_count = training_sample_count @property def validation_sample_count(self): """ Gets the validation_sample_count of this DatasetSummary. Number of samples used for validating the model. :return: The validation_sample_count of this DatasetSummary. :rtype: int """ return self._validation_sample_count @validation_sample_count.setter def validation_sample_count(self, validation_sample_count): """ Sets the validation_sample_count of this DatasetSummary. Number of samples used for validating the model. :param validation_sample_count: The validation_sample_count of this DatasetSummary. :type: int """ self._validation_sample_count = validation_sample_count @property def test_sample_count(self): """ Gets the test_sample_count of this DatasetSummary. Number of samples used for testing the model. :return: The test_sample_count of this DatasetSummary. :rtype: int """ return self._test_sample_count @test_sample_count.setter def test_sample_count(self, test_sample_count): """ Sets the test_sample_count of this DatasetSummary. Number of samples used for testing the model. :param test_sample_count: The test_sample_count of this DatasetSummary. :type: int """ self._test_sample_count = test_sample_count def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other
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