Components

3 Twig Components
5 Render Count
3 ms Render Time
470.0 MiB Memory Usage

Components

Name Metadata Render Count Render Time
ProductState
"App\Twig\Components\ProductState"
components/ProductState.html.twig
2 0.59ms
ProductMostRecent
"App\Twig\Components\ProductMostRecent"
components/ProductMostRecent.html.twig
2 1.76ms
ProductType
"App\Twig\Components\ProductType"
components/ProductType.html.twig
1 0.23ms

Render calls

ProductState App\Twig\Components\ProductState 470.0 MiB 0.32 ms
Input props
[
  "product" => App\Entity\Product\Product {#7310
    #id: 12586
    #code: "IEEE00010231"
    #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
    #variants: Doctrine\ORM\PersistentCollection {#7743 …}
    #options: Doctrine\ORM\PersistentCollection {#7915 …}
    #associations: Doctrine\ORM\PersistentCollection {#7899 …}
    #createdAt: DateTime @1751040529 {#7274
      date: 2025-06-27 18:08:49.0 Europe/Paris (+02:00)
    }
    #updatedAt: DateTime @1754608621 {#7322
      date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
    }
    #enabled: true
    #translations: Doctrine\ORM\PersistentCollection {#7921 …}
    #translationsCache: [
      "en_US" => App\Entity\Product\ProductTranslation {#7920
        #locale: "en_US"
        #translatable: App\Entity\Product\Product {#7310}
        #id: 45353
        #name: "IEEE 2830:2021"
        #slug: "ieee-2830-2021-ieee00010231-244241"
        #description: """
          New IEEE Standard - Active.<br />\n
          The framework and architecture for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a trusted third party are defined in this standard. Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard.<br />\n
          \t\t\t\t<br />\n
          This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE). A distinctive feature of this technique is the essential use of a third-party TEE for computations. The standard specifies functional components, workflows, security requirements, technicalrequirements, and protocols.<br />\n
          There are many use cases in industries ranging from finance to healthcare to education where practitioners wish to apply machine learning to data sets that are aggregated from sources that cannot or should not be combined due to regulatory, competitive, or ethical considerations. Two fundamentally different approaches exist for addressing this: federated machine learning and shared machine learning (SML). In federated machine learning, models are constructed by training local models on local data samples and exchanging intermediate parameters (e.g., the weights generated for a neural network or bases for a vector space that defines an embedding) among multiple parties to generate a global model shared by the participants. In trusted execution environment (TEE) based SML, the data are shared but are encrypted and given to a trusted third party to construct a model that is then shared. This standard will provide a verifiable basis for trust and security.
          """
        #metaKeywords: null
        #metaDescription: null
        #shortDescription: "IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning"
        -notes: "Active"
      }
    ]
    #currentLocale: "en_US"
    #currentTranslation: null
    #fallbackLocale: "en_US"
    #variantSelectionMethod: "match"
    #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
    #channels: Doctrine\ORM\PersistentCollection {#7627 …}
    #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
    #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
    #averageRating: 0.0
    #images: Doctrine\ORM\PersistentCollection {#7644 …}
    -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
    -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
    -apiLastModifiedAt: DateTime @1754517600 {#7317
      date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
    }
    -lastUpdatedAt: DateTime @1637535600 {#7292
      date: 2021-11-22 00:00:00.0 Europe/Paris (+01:00)
    }
    -author: ""
    -publishedAt: DateTime @1634853600 {#7318
      date: 2021-10-22 00:00:00.0 Europe/Paris (+02:00)
    }
    -releasedAt: null
    -confirmedAt: null
    -canceledAt: null
    -edition: null
    -coreDocument: "2830"
    -bookCollection: ""
    -pageCount: 23
    -documents: Doctrine\ORM\PersistentCollection {#7464 …}
    -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
  }
  "showFullLabel" => "true"
]
Attributes
[
  "showFullLabel" => "true"
]
Component
App\Twig\Components\ProductState {#93054
  +product: App\Entity\Product\Product {#7310
    #id: 12586
    #code: "IEEE00010231"
    #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
    #variants: Doctrine\ORM\PersistentCollection {#7743 …}
    #options: Doctrine\ORM\PersistentCollection {#7915 …}
    #associations: Doctrine\ORM\PersistentCollection {#7899 …}
    #createdAt: DateTime @1751040529 {#7274
      date: 2025-06-27 18:08:49.0 Europe/Paris (+02:00)
    }
    #updatedAt: DateTime @1754608621 {#7322
      date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
    }
    #enabled: true
    #translations: Doctrine\ORM\PersistentCollection {#7921 …}
    #translationsCache: [
      "en_US" => App\Entity\Product\ProductTranslation {#7920
        #locale: "en_US"
        #translatable: App\Entity\Product\Product {#7310}
        #id: 45353
        #name: "IEEE 2830:2021"
        #slug: "ieee-2830-2021-ieee00010231-244241"
        #description: """
          New IEEE Standard - Active.<br />\n
          The framework and architecture for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a trusted third party are defined in this standard. Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard.<br />\n
          \t\t\t\t<br />\n
          This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE). A distinctive feature of this technique is the essential use of a third-party TEE for computations. The standard specifies functional components, workflows, security requirements, technicalrequirements, and protocols.<br />\n
          There are many use cases in industries ranging from finance to healthcare to education where practitioners wish to apply machine learning to data sets that are aggregated from sources that cannot or should not be combined due to regulatory, competitive, or ethical considerations. Two fundamentally different approaches exist for addressing this: federated machine learning and shared machine learning (SML). In federated machine learning, models are constructed by training local models on local data samples and exchanging intermediate parameters (e.g., the weights generated for a neural network or bases for a vector space that defines an embedding) among multiple parties to generate a global model shared by the participants. In trusted execution environment (TEE) based SML, the data are shared but are encrypted and given to a trusted third party to construct a model that is then shared. This standard will provide a verifiable basis for trust and security.
          """
        #metaKeywords: null
        #metaDescription: null
        #shortDescription: "IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning"
        -notes: "Active"
      }
    ]
    #currentLocale: "en_US"
    #currentTranslation: null
    #fallbackLocale: "en_US"
    #variantSelectionMethod: "match"
    #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
    #channels: Doctrine\ORM\PersistentCollection {#7627 …}
    #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
    #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
    #averageRating: 0.0
    #images: Doctrine\ORM\PersistentCollection {#7644 …}
    -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
    -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
    -apiLastModifiedAt: DateTime @1754517600 {#7317
      date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
    }
    -lastUpdatedAt: DateTime @1637535600 {#7292
      date: 2021-11-22 00:00:00.0 Europe/Paris (+01:00)
    }
    -author: ""
    -publishedAt: DateTime @1634853600 {#7318
      date: 2021-10-22 00:00:00.0 Europe/Paris (+02:00)
    }
    -releasedAt: null
    -confirmedAt: null
    -canceledAt: null
    -edition: null
    -coreDocument: "2830"
    -bookCollection: ""
    -pageCount: 23
    -documents: Doctrine\ORM\PersistentCollection {#7464 …}
    -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
  }
  +appearance: "state-active"
  +labels: [
    "Active"
  ]
  -stateAttributeCode: "state"
  -localeContext: Sylius\Component\Locale\Context\CompositeLocaleContext {#1833 …}
}
ProductType App\Twig\Components\ProductType 470.0 MiB 0.23 ms
Input props
[
  "product" => App\Entity\Product\Product {#7310
    #id: 12586
    #code: "IEEE00010231"
    #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
    #variants: Doctrine\ORM\PersistentCollection {#7743 …}
    #options: Doctrine\ORM\PersistentCollection {#7915 …}
    #associations: Doctrine\ORM\PersistentCollection {#7899 …}
    #createdAt: DateTime @1751040529 {#7274
      date: 2025-06-27 18:08:49.0 Europe/Paris (+02:00)
    }
    #updatedAt: DateTime @1754608621 {#7322
      date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
    }
    #enabled: true
    #translations: Doctrine\ORM\PersistentCollection {#7921 …}
    #translationsCache: [
      "en_US" => App\Entity\Product\ProductTranslation {#7920
        #locale: "en_US"
        #translatable: App\Entity\Product\Product {#7310}
        #id: 45353
        #name: "IEEE 2830:2021"
        #slug: "ieee-2830-2021-ieee00010231-244241"
        #description: """
          New IEEE Standard - Active.<br />\n
          The framework and architecture for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a trusted third party are defined in this standard. Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard.<br />\n
          \t\t\t\t<br />\n
          This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE). A distinctive feature of this technique is the essential use of a third-party TEE for computations. The standard specifies functional components, workflows, security requirements, technicalrequirements, and protocols.<br />\n
          There are many use cases in industries ranging from finance to healthcare to education where practitioners wish to apply machine learning to data sets that are aggregated from sources that cannot or should not be combined due to regulatory, competitive, or ethical considerations. Two fundamentally different approaches exist for addressing this: federated machine learning and shared machine learning (SML). In federated machine learning, models are constructed by training local models on local data samples and exchanging intermediate parameters (e.g., the weights generated for a neural network or bases for a vector space that defines an embedding) among multiple parties to generate a global model shared by the participants. In trusted execution environment (TEE) based SML, the data are shared but are encrypted and given to a trusted third party to construct a model that is then shared. This standard will provide a verifiable basis for trust and security.
          """
        #metaKeywords: null
        #metaDescription: null
        #shortDescription: "IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning"
        -notes: "Active"
      }
    ]
    #currentLocale: "en_US"
    #currentTranslation: null
    #fallbackLocale: "en_US"
    #variantSelectionMethod: "match"
    #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
    #channels: Doctrine\ORM\PersistentCollection {#7627 …}
    #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
    #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
    #averageRating: 0.0
    #images: Doctrine\ORM\PersistentCollection {#7644 …}
    -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
    -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
    -apiLastModifiedAt: DateTime @1754517600 {#7317
      date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
    }
    -lastUpdatedAt: DateTime @1637535600 {#7292
      date: 2021-11-22 00:00:00.0 Europe/Paris (+01:00)
    }
    -author: ""
    -publishedAt: DateTime @1634853600 {#7318
      date: 2021-10-22 00:00:00.0 Europe/Paris (+02:00)
    }
    -releasedAt: null
    -confirmedAt: null
    -canceledAt: null
    -edition: null
    -coreDocument: "2830"
    -bookCollection: ""
    -pageCount: 23
    -documents: Doctrine\ORM\PersistentCollection {#7464 …}
    -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
  }
]
Attributes
[]
Component
App\Twig\Components\ProductType {#93234
  +product: App\Entity\Product\Product {#7310
    #id: 12586
    #code: "IEEE00010231"
    #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
    #variants: Doctrine\ORM\PersistentCollection {#7743 …}
    #options: Doctrine\ORM\PersistentCollection {#7915 …}
    #associations: Doctrine\ORM\PersistentCollection {#7899 …}
    #createdAt: DateTime @1751040529 {#7274
      date: 2025-06-27 18:08:49.0 Europe/Paris (+02:00)
    }
    #updatedAt: DateTime @1754608621 {#7322
      date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
    }
    #enabled: true
    #translations: Doctrine\ORM\PersistentCollection {#7921 …}
    #translationsCache: [
      "en_US" => App\Entity\Product\ProductTranslation {#7920
        #locale: "en_US"
        #translatable: App\Entity\Product\Product {#7310}
        #id: 45353
        #name: "IEEE 2830:2021"
        #slug: "ieee-2830-2021-ieee00010231-244241"
        #description: """
          New IEEE Standard - Active.<br />\n
          The framework and architecture for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a trusted third party are defined in this standard. Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard.<br />\n
          \t\t\t\t<br />\n
          This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE). A distinctive feature of this technique is the essential use of a third-party TEE for computations. The standard specifies functional components, workflows, security requirements, technicalrequirements, and protocols.<br />\n
          There are many use cases in industries ranging from finance to healthcare to education where practitioners wish to apply machine learning to data sets that are aggregated from sources that cannot or should not be combined due to regulatory, competitive, or ethical considerations. Two fundamentally different approaches exist for addressing this: federated machine learning and shared machine learning (SML). In federated machine learning, models are constructed by training local models on local data samples and exchanging intermediate parameters (e.g., the weights generated for a neural network or bases for a vector space that defines an embedding) among multiple parties to generate a global model shared by the participants. In trusted execution environment (TEE) based SML, the data are shared but are encrypted and given to a trusted third party to construct a model that is then shared. This standard will provide a verifiable basis for trust and security.
          """
        #metaKeywords: null
        #metaDescription: null
        #shortDescription: "IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning"
        -notes: "Active"
      }
    ]
    #currentLocale: "en_US"
    #currentTranslation: null
    #fallbackLocale: "en_US"
    #variantSelectionMethod: "match"
    #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
    #channels: Doctrine\ORM\PersistentCollection {#7627 …}
    #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
    #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
    #averageRating: 0.0
    #images: Doctrine\ORM\PersistentCollection {#7644 …}
    -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
    -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
    -apiLastModifiedAt: DateTime @1754517600 {#7317
      date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
    }
    -lastUpdatedAt: DateTime @1637535600 {#7292
      date: 2021-11-22 00:00:00.0 Europe/Paris (+01:00)
    }
    -author: ""
    -publishedAt: DateTime @1634853600 {#7318
      date: 2021-10-22 00:00:00.0 Europe/Paris (+02:00)
    }
    -releasedAt: null
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    -canceledAt: null
    -edition: null
    -coreDocument: "2830"
    -bookCollection: ""
    -pageCount: 23
    -documents: Doctrine\ORM\PersistentCollection {#7464 …}
    -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
  }
  +label: "Standard"
  -typeAttributeCode: "type"
  -localeContext: Sylius\Component\Locale\Context\CompositeLocaleContext {#1833 …}
}
ProductMostRecent App\Twig\Components\ProductMostRecent 470.0 MiB 0.74 ms
Input props
[
  "product" => App\Entity\Product\Product {#7310
    #id: 12586
    #code: "IEEE00010231"
    #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
    #variants: Doctrine\ORM\PersistentCollection {#7743 …}
    #options: Doctrine\ORM\PersistentCollection {#7915 …}
    #associations: Doctrine\ORM\PersistentCollection {#7899 …}
    #createdAt: DateTime @1751040529 {#7274
      date: 2025-06-27 18:08:49.0 Europe/Paris (+02:00)
    }
    #updatedAt: DateTime @1754608621 {#7322
      date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
    }
    #enabled: true
    #translations: Doctrine\ORM\PersistentCollection {#7921 …}
    #translationsCache: [
      "en_US" => App\Entity\Product\ProductTranslation {#7920
        #locale: "en_US"
        #translatable: App\Entity\Product\Product {#7310}
        #id: 45353
        #name: "IEEE 2830:2021"
        #slug: "ieee-2830-2021-ieee00010231-244241"
        #description: """
          New IEEE Standard - Active.<br />\n
          The framework and architecture for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a trusted third party are defined in this standard. Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard.<br />\n
          \t\t\t\t<br />\n
          This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE). A distinctive feature of this technique is the essential use of a third-party TEE for computations. The standard specifies functional components, workflows, security requirements, technicalrequirements, and protocols.<br />\n
          There are many use cases in industries ranging from finance to healthcare to education where practitioners wish to apply machine learning to data sets that are aggregated from sources that cannot or should not be combined due to regulatory, competitive, or ethical considerations. Two fundamentally different approaches exist for addressing this: federated machine learning and shared machine learning (SML). In federated machine learning, models are constructed by training local models on local data samples and exchanging intermediate parameters (e.g., the weights generated for a neural network or bases for a vector space that defines an embedding) among multiple parties to generate a global model shared by the participants. In trusted execution environment (TEE) based SML, the data are shared but are encrypted and given to a trusted third party to construct a model that is then shared. This standard will provide a verifiable basis for trust and security.
          """
        #metaKeywords: null
        #metaDescription: null
        #shortDescription: "IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning"
        -notes: "Active"
      }
    ]
    #currentLocale: "en_US"
    #currentTranslation: null
    #fallbackLocale: "en_US"
    #variantSelectionMethod: "match"
    #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
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    #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
    #averageRating: 0.0
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    -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
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]
Attributes
[]
Component
App\Twig\Components\ProductMostRecent {#93309
  +product: App\Entity\Product\Product {#7310
    #id: 12586
    #code: "IEEE00010231"
    #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
    #variants: Doctrine\ORM\PersistentCollection {#7743 …}
    #options: Doctrine\ORM\PersistentCollection {#7915 …}
    #associations: Doctrine\ORM\PersistentCollection {#7899 …}
    #createdAt: DateTime @1751040529 {#7274
      date: 2025-06-27 18:08:49.0 Europe/Paris (+02:00)
    }
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      date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
    }
    #enabled: true
    #translations: Doctrine\ORM\PersistentCollection {#7921 …}
    #translationsCache: [
      "en_US" => App\Entity\Product\ProductTranslation {#7920
        #locale: "en_US"
        #translatable: App\Entity\Product\Product {#7310}
        #id: 45353
        #name: "IEEE 2830:2021"
        #slug: "ieee-2830-2021-ieee00010231-244241"
        #description: """
          New IEEE Standard - Active.<br />\n
          The framework and architecture for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a trusted third party are defined in this standard. Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard.<br />\n
          \t\t\t\t<br />\n
          This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE). A distinctive feature of this technique is the essential use of a third-party TEE for computations. The standard specifies functional components, workflows, security requirements, technicalrequirements, and protocols.<br />\n
          There are many use cases in industries ranging from finance to healthcare to education where practitioners wish to apply machine learning to data sets that are aggregated from sources that cannot or should not be combined due to regulatory, competitive, or ethical considerations. Two fundamentally different approaches exist for addressing this: federated machine learning and shared machine learning (SML). In federated machine learning, models are constructed by training local models on local data samples and exchanging intermediate parameters (e.g., the weights generated for a neural network or bases for a vector space that defines an embedding) among multiple parties to generate a global model shared by the participants. In trusted execution environment (TEE) based SML, the data are shared but are encrypted and given to a trusted third party to construct a model that is then shared. This standard will provide a verifiable basis for trust and security.
          """
        #metaKeywords: null
        #metaDescription: null
        #shortDescription: "IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning"
        -notes: "Active"
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    #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
    #averageRating: 0.0
    #images: Doctrine\ORM\PersistentCollection {#7644 …}
    -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
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    -apiLastModifiedAt: DateTime @1754517600 {#7317
      date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
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    -lastUpdatedAt: DateTime @1637535600 {#7292
      date: 2021-11-22 00:00:00.0 Europe/Paris (+01:00)
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    -author: ""
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    -documents: Doctrine\ORM\PersistentCollection {#7464 …}
    -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
  }
  +label: "Most Recent"
  +icon: "check-xs"
  -mostRecentAttributeCode: "most_recent"
  -localeContext: Sylius\Component\Locale\Context\CompositeLocaleContext {#1833 …}
}
ProductState App\Twig\Components\ProductState 470.0 MiB 0.27 ms
Input props
[
  "product" => App\Entity\Product\Product {#7310
    #id: 12586
    #code: "IEEE00010231"
    #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
    #variants: Doctrine\ORM\PersistentCollection {#7743 …}
    #options: Doctrine\ORM\PersistentCollection {#7915 …}
    #associations: Doctrine\ORM\PersistentCollection {#7899 …}
    #createdAt: DateTime @1751040529 {#7274
      date: 2025-06-27 18:08:49.0 Europe/Paris (+02:00)
    }
    #updatedAt: DateTime @1754608621 {#7322
      date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
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