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1.  final def !=(arg0: Any): Boolean

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2.  final def ##: Int

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3.  final def ==(arg0: Any): Boolean

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4.  def apply[S](numBlocks: Int, in: Int, attentionHiddenPerHeadDim: Int, attentionNumHeads: Int, mlpHiddenDim: Int, dropout: Double, padToken: Long, tOpt: STenOptions, linearized: Boolean)(implicit arg0: Sc[S]): TransformerEncoder

   Factory for the encoder module of transformer Does *not* include embedding and positional encoding

   Factory for the encoder module of transformer Does *not* include embedding and positional encoding

   Input is (data, tokens) where data is (batch, num tokens, in dimension), double tensor tokens is (batch,num tokens) long tensor.

   The sole purpose of tokens is to carry over the padding

   numBlocks

   number of transformer blocks to create

   in

   input dimension

   attentionHiddenPerHeadDim

   size of hidden attention dimension of each attention head

   attentionNumHeads

   number of attention heads

   mlpHiddenDim

   size of hidden dimension of the two layer perceptron

   dropout

   dropout rate

   padToken

   pad token, (batch, seq) positions where tokens == padToken are ignored

   tOpt

   tensor options

   returns

   a module

5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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7.  final def eq(arg0: AnyRef): Boolean

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8.  def equals(arg0: AnyRef): Boolean

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9.  def finalize(): Unit

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10.  final def getClass(): Class[_ <: AnyRef]

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11.  def hashCode(): Int

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12.  final def isInstanceOf[T0]: Boolean

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13.  implicit val load: Load[TransformerEncoder]
14.  final def ne(arg0: AnyRef): Boolean

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15.  final def notify(): Unit

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16.  final def notifyAll(): Unit

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17.  final def synchronized[T0](arg0: => T0): T0

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18.  def toString(): String

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19.  implicit val trainingMode: TrainingMode[TransformerEncoder]
20.  final def wait(): Unit

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21.  final def wait(arg0: Long, arg1: Int): Unit

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22.  final def wait(arg0: Long): Unit

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