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    LiteRtError

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    pub suberror LiteRtError {
      LiteRtError(String)
    }

    LiteRtError::to_string

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    fn LiteRtError::to_string(self : LiteRtError) -> String

    LiteRtLoweredValue

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    pub struct LiteRtLoweredValue[T] {
      name_ : String
      tensor_ : T
    }

    LiteRtLoweredValue::name

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    fn[T] LiteRtLoweredValue::name(self : LiteRtLoweredValue[T]) -> String

    LiteRtLoweredValue::tensor

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    fn[T] LiteRtLoweredValue::tensor(self : LiteRtLoweredValue[T]) -> T

    LiteRtModel

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    pub struct LiteRtModel {
      values_ : Array[LiteRtValue]
      nodes_ : Array[LiteRtNode]
      output_names_ : Array[String]
    }

    LiteRtModel::inputs

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    fn LiteRtModel::inputs(self : LiteRtModel) -> Array[LiteRtValue]

    LiteRtModel::lower

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    fn[T : 
    TensorOps    ] LiteRtModel::lower(self : LiteRtModel, make_input : (String, 
    Shape    ) -> T raise, make_constant : (String, 
    Shape    , Array[Float]) -> T raise) -> Array[LiteRtLoweredValue[T]] raise

    LiteRtModel::new

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    fn LiteRtModel::new(values : Array[LiteRtValue], nodes : Array[LiteRtNode], output_names : Array[String]) -> LiteRtModel raise LiteRtError

    LiteRtNode

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    pub(all) enum LiteRtNode {
      Add(String, String, String)
      Sub(String, String, String)
      Mul(String, String, String)
      Div(String, String, String)
      ReduceMean(String, String, Array[Int], Bool)
      Gather(String, String, Array[Int], 
    Shape    , Int)
      Slice(String, String, Array[Int], Array[Int])
      Gelu(String, String)
      LayerNormalization(String, String, String, String, Array[Int], Float)
      Concat(String, String, String, Int)
      Matmul(String, String, String)
      Conv2d(String, String, String, 
    Conv2dOptions    )
      MaxPool2d(String, String, 
    Pool2dOptions    )
      AveragePool2d(String, String, 
    Pool2dOptions    )
      Sigmoid(String, String)
      Tanh(String, String)
      Clamp(String, String, Float, Float)
      Relu(String, String)
      Softmax(String, String, Int)
      Reshape(String, String)
      Transpose(String, String, Array[Int])
    }

    LiteRtValue

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    pub struct LiteRtValue {
      name_ : String
      shape_ : 
    Shape
      source_ : LiteRtValueSource
    }

    LiteRtValue::constant

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    fn LiteRtValue::constant(name : String, shape : 
    Shape    , values : Array[Float]) -> LiteRtValue raise LiteRtError

    LiteRtValue::input

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    fn LiteRtValue::input(name : String, shape : 
    Shape    ) -> LiteRtValue raise LiteRtError

    LiteRtValue::intermediate

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    fn LiteRtValue::intermediate(name : String, shape : 
    Shape    ) -> LiteRtValue raise LiteRtError

    LiteRtValue::name

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    fn LiteRtValue::name(self : LiteRtValue) -> String

    LiteRtValue::shape

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    LiteRtValueSource

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    pub enum LiteRtValueSource {
      Input
      Constant(Array[Float])
      Intermediate
    }

    TfliteModel

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    pub(all) struct TfliteModel {
    }

    TfliteModel::parse

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    fn TfliteModel::parse(bytes : Bytes) -> LiteRtModel raise LiteRtError

    TfliteModel::parse_runtime

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    fn TfliteModel::parse_runtime(bytes : Bytes) -> TfliteRuntimeModel raise LiteRtError

    Parse a TFLite model while retaining UINT8/INT8 runtime I/O metadata.

    TfliteRuntimeDataType

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    pub enum TfliteRuntimeDataType {
      Float32
      UInt8(Array[Float], Array[Int], Int)
      Int8(Array[Float], Array[Int], Int)
    }

    TfliteRuntimeModel

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    pub struct TfliteRuntimeModel {
      model_ : LiteRtModel
      inputs_ : Array[TfliteRuntimeTensor]
      outputs_ : Array[TfliteRuntimeTensor]
    }

    TfliteRuntimeModel::inputs

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    TfliteRuntimeModel::model

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    TfliteRuntimeModel::outputs

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    TfliteRuntimeTensor

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    pub struct TfliteRuntimeTensor {
      name_ : String
      shape_ : 
    Shape
      data_type_ : TfliteRuntimeDataType
    }

    TfliteRuntimeTensor::data_type

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    TfliteRuntimeTensor::dequantize

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    fn TfliteRuntimeTensor::dequantize(self : TfliteRuntimeTensor, values : Array[Int]) -> Array[Float] raise LiteRtError

    Dequantize raw runtime values according to this tensor's TFLite metadata.

    TfliteRuntimeTensor::name

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    fn TfliteRuntimeTensor::name(self : TfliteRuntimeTensor) -> String

    TfliteRuntimeTensor::requantize

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    fn TfliteRuntimeTensor::requantize(self : TfliteRuntimeTensor, values : Array[Float]) -> Array[Int] raise LiteRtError

    Requantize float32 backend results according to this tensor's TFLite metadata.

    TfliteRuntimeTensor::shape

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    dequantize_int8

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    fn dequantize_int8(values : Array[Int], scale : Float, zero_point : Int) -> Array[Float] raise LiteRtError

    Convert signed 8-bit affine values to their float32 values.

    dequantize_uint8

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    fn dequantize_uint8(values : Array[Int], scale : Float, zero_point : Int) -> Array[Float] raise LiteRtError

    Convert unsigned 8-bit affine values to their float32 values.

    requantize_int8

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    fn requantize_int8(values : Array[Float], scale : Float, zero_point : Int) -> Array[Int] raise LiteRtError

    Convert float32 values to signed 8-bit affine values with saturation.

    requantize_uint8

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    fn requantize_uint8(values : Array[Float], scale : Float, zero_point : Int) -> Array[Int] raise LiteRtError

    Convert float32 values to unsigned 8-bit affine values with saturation.

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