Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 297909837 Change-Id: Ic30a2b2c725f6815818ce0f61748f0dd8f6c38ac
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A. Unique TensorFlower
TensorFlower Gardener
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8443eb5ce4
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@ -3580,7 +3580,7 @@ func BoostedTreesSparseCalculateBestFeatureSplitSplitType(value string) BoostedT
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// l1: l1 regularization factor on leaf weights, per instance based.
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// l2: l2 regularization factor on leaf weights, per instance based.
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// tree_complexity: adjustment to the gain, per leaf based.
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// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
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// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
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// logits_dimension: The dimension of logit, i.e., number of classes.
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//
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// Returns:
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@ -3677,7 +3677,7 @@ func BoostedTreesCalculateBestFeatureSplitV2(scope *Scope, node_id_range tf.Outp
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// l1: l1 regularization factor on leaf weights, per instance based.
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// l2: l2 regularization factor on leaf weights, per instance based.
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// tree_complexity: adjustment to the gain, per leaf based.
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// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
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// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
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// max_splits: the number of nodes that can be split in the whole tree. Used as a dimension of output tensors.
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//
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// Returns:
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@ -3730,7 +3730,7 @@ func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Out
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// Checks whether a tree ensemble has been initialized.
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//
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// Arguments:
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// tree_ensemble_handle: Handle to the tree ensemble resouce.
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// tree_ensemble_handle: Handle to the tree ensemble resource.
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//
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// Returns output boolean on whether it is initialized or not.
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func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) {
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@ -5126,7 +5126,7 @@ func CudnnRNNParamsToCanonicalV2NumProj(value int64) CudnnRNNParamsToCanonicalV2
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// num_layers: Specifies the number of layers in the RNN model.
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// num_units: Specifies the size of the hidden state.
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// input_size: Specifies the size of the input state.
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// num_params_weigths: number of weight parameter matrix for all layers.
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// num_params_weights: number of weight parameter matrix for all layers.
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// num_params_biases: number of bias parameter vector for all layers.
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// weights: the canonical form of weights that can be used for saving
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// and restoration. They are more likely to be compatible across different
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@ -8344,7 +8344,7 @@ func BoostedTreesCalculateBestFeatureSplitSplitType(value string) BoostedTreesCa
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// l1: l1 regularization factor on leaf weights, per instance based.
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// l2: l2 regularization factor on leaf weights, per instance based.
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// tree_complexity: adjustment to the gain, per leaf based.
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// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
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// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
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// logits_dimension: The dimension of logit, i.e., number of classes.
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//
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// Returns:
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@ -13731,7 +13731,7 @@ func DebugNumericSummaryV2OutputDtype(value tf.DataType) DebugNumericSummaryV2At
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// element is a bit which is set to 1 if the input tensor has an
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// infinity or nan value, or zero otherwise.
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//
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// 3 (CONCISE_HEALTH): Ouput a float32/64 tensor of shape [5]. The 1st
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// 3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st
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// element is the tensor_id, if provided, and -1 otherwise. The
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// remaining four slots are the total number of elements, -infs,
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// +infs, and nans in the input tensor respectively.
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@ -14089,11 +14089,11 @@ func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional
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//
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// Arguments:
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// superdiag: Tensor of shape `[..., 1, M]`, representing superdiagonals of
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// tri-diagonal matrices to the left of multiplication. Last element is ingored.
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// tri-diagonal matrices to the left of multiplication. Last element is ignored.
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// maindiag: Tensor of shape `[..., 1, M]`, representing main diagonals of tri-diagonal
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// matrices to the left of multiplication.
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// subdiag: Tensor of shape `[..., 1, M]`, representing subdiagonals of tri-diagonal
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// matrices to the left of multiplication. First element is ingored.
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// matrices to the left of multiplication. First element is ignored.
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// rhs: Tensor of shape `[..., M, N]`, representing MxN matrices to the right of
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// multiplication.
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//
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@ -17710,7 +17710,7 @@ func CudnnRNNCanonicalToParamsV2NumProj(value int64) CudnnRNNCanonicalToParamsV2
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// biases: the canonical form of biases that can be used for saving
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// and restoration. They are more likely to be compatible across different
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// generations.
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// num_params_weigths: number of weight parameter matrix for all layers.
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// num_params_weights: number of weight parameter matrix for all layers.
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// num_params_biases: number of bias parameter vector for all layers.
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// rnn_mode: Indicates the type of the RNN model.
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// input_mode: Indicate whether there is a linear projection between the input and
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@ -31238,8 +31238,8 @@ func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
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// linear: Should be from a Variable().
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// grad: The gradient.
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// lr: Scaling factor. Must be a scalar.
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// l1: L1 regulariation. Must be a scalar.
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// l2: L2 shrinkage regulariation. Must be a scalar.
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// l1: L1 regularization. Must be a scalar.
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// l2: L2 shrinkage regularization. Must be a scalar.
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//
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// lr_power: Scaling factor. Must be a scalar.
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//
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@ -36706,8 +36706,8 @@ func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
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// linear: Should be from a Variable().
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// grad: The gradient.
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// lr: Scaling factor. Must be a scalar.
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// l1: L1 regulariation. Must be a scalar.
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// l2: L2 regulariation. Must be a scalar.
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// l1: L1 regularization. Must be a scalar.
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// l2: L2 regularization. Must be a scalar.
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// lr_power: Scaling factor. Must be a scalar.
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//
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// Returns the created operation.
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@ -43228,7 +43228,7 @@ func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2At
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// indices: A vector of indices into the first dimension of var and accum.
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// lr: Scaling factor. Must be a scalar.
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// l1: L1 regularization. Must be a scalar.
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// l2: L2 shrinkage regulariation. Must be a scalar.
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// l2: L2 shrinkage regularization. Must be a scalar.
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//
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// lr_power: Scaling factor. Must be a scalar.
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//
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