aws_sdk_cleanroomsml/operation/create_training_dataset/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_training_dataset::_create_training_dataset_output::CreateTrainingDatasetOutputBuilder;

pub use crate::operation::create_training_dataset::_create_training_dataset_input::CreateTrainingDatasetInputBuilder;

impl crate::operation::create_training_dataset::builders::CreateTrainingDatasetInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::create_training_dataset::CreateTrainingDatasetOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_training_dataset::CreateTrainingDatasetError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_training_dataset();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateTrainingDataset`.
///
/// <p>Defines the information necessary to create a training dataset. In Clean Rooms ML, the <code>TrainingDataset</code> is metadata that points to a Glue table, which is read only during <code>AudienceModel</code> creation.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateTrainingDatasetFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_training_dataset::builders::CreateTrainingDatasetInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_training_dataset::CreateTrainingDatasetOutput,
        crate::operation::create_training_dataset::CreateTrainingDatasetError,
    > for CreateTrainingDatasetFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_training_dataset::CreateTrainingDatasetOutput,
            crate::operation::create_training_dataset::CreateTrainingDatasetError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateTrainingDatasetFluentBuilder {
    /// Creates a new `CreateTrainingDatasetFluentBuilder`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the CreateTrainingDataset as a reference.
    pub fn as_input(&self) -> &crate::operation::create_training_dataset::builders::CreateTrainingDatasetInputBuilder {
        &self.inner
    }
    /// Sends the request and returns the response.
    ///
    /// If an error occurs, an `SdkError` will be returned with additional details that
    /// can be matched against.
    ///
    /// By default, any retryable failures will be retried twice. Retry behavior
    /// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> ::std::result::Result<
        crate::operation::create_training_dataset::CreateTrainingDatasetOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_training_dataset::CreateTrainingDatasetError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::create_training_dataset::CreateTrainingDataset::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_training_dataset::CreateTrainingDataset::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::create_training_dataset::CreateTrainingDatasetOutput,
        crate::operation::create_training_dataset::CreateTrainingDatasetError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
        self.set_config_override(::std::option::Option::Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>The name of the training dataset. This name must be unique in your account and region.</p>
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.name(input.into());
        self
    }
    /// <p>The name of the training dataset. This name must be unique in your account and region.</p>
    pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_name(input);
        self
    }
    /// <p>The name of the training dataset. This name must be unique in your account and region.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_name()
    }
    /// <p>The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the <code>dataSource</code> field of each dataset.</p>
    /// <p>Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an <code>AccessDeniedException</code> error.</p>
    pub fn role_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.role_arn(input.into());
        self
    }
    /// <p>The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the <code>dataSource</code> field of each dataset.</p>
    /// <p>Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an <code>AccessDeniedException</code> error.</p>
    pub fn set_role_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_role_arn(input);
        self
    }
    /// <p>The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the <code>dataSource</code> field of each dataset.</p>
    /// <p>Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an <code>AccessDeniedException</code> error.</p>
    pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_role_arn()
    }
    ///
    /// Appends an item to `trainingData`.
    ///
    /// To override the contents of this collection use [`set_training_data`](Self::set_training_data).
    ///
    /// <p>An array of information that lists the Dataset objects, which specifies the dataset type and details on its location and schema. You must provide a role that has read access to these tables.</p>
    pub fn training_data(mut self, input: crate::types::Dataset) -> Self {
        self.inner = self.inner.training_data(input);
        self
    }
    /// <p>An array of information that lists the Dataset objects, which specifies the dataset type and details on its location and schema. You must provide a role that has read access to these tables.</p>
    pub fn set_training_data(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Dataset>>) -> Self {
        self.inner = self.inner.set_training_data(input);
        self
    }
    /// <p>An array of information that lists the Dataset objects, which specifies the dataset type and details on its location and schema. You must provide a role that has read access to these tables.</p>
    pub fn get_training_data(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Dataset>> {
        self.inner.get_training_data()
    }
    ///
    /// Adds a key-value pair to `tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn tags(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.tags(k.into(), v.into());
        self
    }
    /// <p>The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
    /// <p>The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn get_tags(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_tags()
    }
    /// <p>The description of the training dataset.</p>
    pub fn description(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.description(input.into());
        self
    }
    /// <p>The description of the training dataset.</p>
    pub fn set_description(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_description(input);
        self
    }
    /// <p>The description of the training dataset.</p>
    pub fn get_description(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_description()
    }
}