Package | Description |
---|---|
org.bytedeco.pytorch | |
org.bytedeco.pytorch.global |
Modifier and Type | Method and Description |
---|---|
T_TensorTensor_T |
Tensor.aminmax() |
T_TensorTensor_T |
Tensor.aminmax(LongOptional dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.cummax(Dimname dim) |
T_TensorTensor_T |
Tensor.cummax(long dim) |
T_TensorTensor_T |
Tensor.cummin(Dimname dim) |
T_TensorTensor_T |
Tensor.cummin(long dim) |
T_TensorTensor_T |
AdaptiveMaxPool2dImpl.forward_with_indices(Tensor input)
Returns the indices along with the outputs.
|
T_TensorTensor_T |
FractionalMaxPool3dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
T_TensorTensor_T |
FractionalMaxPool2dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
T_TensorTensor_T |
MaxPool2dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
T_TensorTensor_T |
AdaptiveMaxPool1dImpl.forward_with_indices(Tensor input)
Returns the indices along with the outputs.
|
T_TensorTensor_T |
MaxPool1dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
T_TensorTensor_T |
MaxPool3dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
T_TensorTensor_T |
AdaptiveMaxPool3dImpl.forward_with_indices(Tensor input)
Returns the indices along with the outputs.
|
T_TensorTensor_T |
LSTMCellImpl.forward(Tensor input) |
T_TensorTensor_T |
RNNImpl.forward(Tensor input) |
T_TensorTensor_T |
GRUImpl.forward(Tensor input) |
T_TensorTensor_T |
LSTMCellImpl.forward(Tensor input,
T_TensorTensor_TOptional hx_opt) |
T_TensorTensor_T |
RNNImpl.forward(Tensor input,
Tensor hx) |
T_TensorTensor_T |
GRUImpl.forward(Tensor input,
Tensor hx) |
T_TensorTensor_T |
MultiheadAttentionImpl.forward(Tensor query,
Tensor key,
Tensor value) |
T_TensorTensor_T |
MultiheadAttentionImpl.forward(Tensor query,
Tensor key,
Tensor value,
Tensor key_padding_mask,
boolean need_weights,
Tensor attn_mask,
boolean average_attn_weights) |
T_TensorTensor_T |
AnyModule.forwardT_TensorTensor_T(Tensor input) |
T_TensorTensor_T |
SequentialImpl.forwardT_TensorTensor_T(Tensor input) |
T_TensorTensor_T |
AnyModule.forwardT_TensorTensor_T(Tensor input,
T_TensorTensor_TOptional hx_opt) |
T_TensorTensor_T |
SequentialImpl.forwardT_TensorTensor_T(Tensor input,
T_TensorTensor_TOptional hx_opt) |
T_TensorTensor_T |
AnyModule.forwardT_TensorTensor_T(Tensor input1,
Tensor input2) |
T_TensorTensor_T |
SequentialImpl.forwardT_TensorTensor_T(Tensor input1,
Tensor input2) |
T_TensorTensor_T |
AnyModule.forwardT_TensorTensor_T(Tensor input1,
Tensor input2,
Tensor input3) |
T_TensorTensor_T |
SequentialImpl.forwardT_TensorTensor_T(Tensor input1,
Tensor input2,
Tensor input3) |
T_TensorTensor_T |
AnyModule.forwardT_TensorTensor_T(Tensor query,
Tensor key,
Tensor value,
Tensor key_padding_mask,
boolean need_weights,
Tensor attn_mask,
boolean average_attn_weights) |
T_TensorTensor_T |
SequentialImpl.forwardT_TensorTensor_T(Tensor query,
Tensor key,
Tensor value,
Tensor key_padding_mask,
boolean need_weights,
Tensor attn_mask,
boolean average_attn_weights) |
T_TensorTensor_T |
Tensor.frexp() |
T_TensorTensor_T |
Tensor.geqrf() |
T_TensorTensor_T |
T_TensorTensor_TOptional.get() |
T_TensorTensor_T |
T_TensorT_TensorTensor_T_T.get1() |
T_TensorTensor_T |
T_PackedSequenceT_TensorTensor_T_T.get1() |
static T_TensorTensor_T |
T_PackedSequenceT_TensorTensor_T_T.get1(T_PackedSequenceT_TensorTensor_T_T container) |
static T_TensorTensor_T |
T_TensorT_TensorTensor_T_T.get1(T_TensorT_TensorTensor_T_T container) |
T_TensorTensor_T |
AnyValue.getT_TensorTensor_T() |
T_TensorTensor_T |
Tensor.histogram() |
T_TensorTensor_T |
Tensor.histogram(long bins,
double[] range,
TensorOptional weight,
boolean density) |
T_TensorTensor_T |
Tensor.histogram(long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density) |
T_TensorTensor_T |
Tensor.histogram(Tensor bins) |
T_TensorTensor_T |
Tensor.histogram(Tensor bins,
TensorOptional weight,
boolean density) |
T_TensorTensor_T |
Tensor.kthvalue(long k) |
T_TensorTensor_T |
Tensor.kthvalue(long k,
Dimname dim) |
T_TensorTensor_T |
Tensor.kthvalue(long k,
Dimname dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.kthvalue(long k,
long dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.max(Dimname dim) |
T_TensorTensor_T |
Tensor.max(Dimname dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.max(long dim) |
T_TensorTensor_T |
Tensor.max(long dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.median(Dimname dim) |
T_TensorTensor_T |
Tensor.median(Dimname dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.median(long dim) |
T_TensorTensor_T |
Tensor.median(long dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.min(Dimname dim) |
T_TensorTensor_T |
Tensor.min(Dimname dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.min(long dim) |
T_TensorTensor_T |
Tensor.min(long dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.mode() |
T_TensorTensor_T |
Tensor.mode(Dimname dim) |
T_TensorTensor_T |
Tensor.mode(Dimname dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.mode(long dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.nanmedian(Dimname dim) |
T_TensorTensor_T |
Tensor.nanmedian(Dimname dim,
boolean keepdim) |
T_TensorTensor_T |
Tensor.nanmedian(long dim) |
T_TensorTensor_T |
Tensor.nanmedian(long dim,
boolean keepdim) |
T_TensorTensor_T |
T_TensorTensor_T.put(T_TensorTensor_T x) |
T_TensorTensor_T |
Tensor.qr() |
T_TensorTensor_T |
Tensor.qr(boolean some) |
T_TensorTensor_T |
Tensor.slogdet() |
T_TensorTensor_T |
Tensor.sort() |
T_TensorTensor_T |
Tensor.sort(BoolOptional stable) |
T_TensorTensor_T |
Tensor.sort(BoolOptional stable,
Dimname dim) |
T_TensorTensor_T |
Tensor.sort(BoolOptional stable,
Dimname dim,
boolean descending) |
T_TensorTensor_T |
Tensor.sort(BoolOptional stable,
long dim,
boolean descending) |
T_TensorTensor_T |
Tensor.sort(Dimname dim) |
T_TensorTensor_T |
Tensor.sort(Dimname dim,
boolean descending) |
T_TensorTensor_T |
Tensor.sort(long dim,
boolean descending) |
T_TensorTensor_T |
Tensor.topk_symint(SymInt k) |
T_TensorTensor_T |
Tensor.topk_symint(SymInt k,
long dim,
boolean largest,
boolean sorted) |
T_TensorTensor_T |
Tensor.topk(long k) |
T_TensorTensor_T |
Tensor.topk(long k,
long dim,
boolean largest,
boolean sorted) |
T_TensorTensor_T |
Tensor.triangular_solve(Tensor A) |
T_TensorTensor_T |
Tensor.triangular_solve(Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular) |
T_TensorTensor_T |
AnyValue.try_getT_TensorTensor_T() |
Modifier and Type | Method and Description |
---|---|
static Tensor |
T_TensorTensor_T.get0(T_TensorTensor_T container) |
static Tensor |
T_TensorTensor_T.get1(T_TensorTensor_T container) |
T_TensorTensor_TOptional |
T_TensorTensor_TOptional.put(T_TensorTensor_T value) |
T_TensorTensor_T |
T_TensorTensor_T.put(T_TensorTensor_T x) |
Constructor and Description |
---|
T_TensorT_TensorTensor_T_T(Tensor value0,
T_TensorTensor_T value1) |
T_TensorTensor_TOptional(T_TensorTensor_T value) |
Modifier and Type | Method and Description |
---|---|
static T_TensorTensor_T |
torch.adaptive_max_pool1d_with_indices(Tensor input,
AdaptiveMaxPool1dOptions options)
See the documentation for
torch::nn::functional::AdaptiveMaxPool1dFuncOptions class to learn what
optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.adaptive_max_pool1d_with_indices(Tensor input,
LongPointer output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool1d(Tensor self,
long... output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool1d(Tensor self,
LongArrayRef output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool2d_out(Tensor out,
Tensor indices,
Tensor self,
long... output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool2d_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool2d_outf(Tensor self,
long[] output_size,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.adaptive_max_pool2d_outf(Tensor self,
LongArrayRef output_size,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.adaptive_max_pool2d_with_indices(Tensor input,
AdaptiveMaxPool2dOptions options)
See the documentation for
/**
torch::nn::functional::AdaptiveMaxPool2dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.adaptive_max_pool2d_with_indices(Tensor input,
LongOptional output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool2d(Tensor self,
long... output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool2d(Tensor self,
LongArrayRef output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool3d_out(Tensor out,
Tensor indices,
Tensor self,
long... output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool3d_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool3d_outf(Tensor self,
long[] output_size,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.adaptive_max_pool3d_outf(Tensor self,
LongArrayRef output_size,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.adaptive_max_pool3d_with_indices(Tensor input,
AdaptiveMaxPool3dOptions options)
See the documentation for
/**
torch::nn::functional::AdaptiveMaxPool3dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.adaptive_max_pool3d_with_indices(Tensor input,
LongOptional output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool3d(Tensor self,
long... output_size) |
static T_TensorTensor_T |
torch.adaptive_max_pool3d(Tensor self,
LongArrayRef output_size) |
static T_TensorTensor_T |
torch.aminmax_out(Tensor min,
Tensor max,
Tensor self) |
static T_TensorTensor_T |
torch.aminmax_out(Tensor min,
Tensor max,
Tensor self,
LongOptional dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.aminmax_outf(Tensor self,
LongOptional dim,
boolean keepdim,
Tensor min,
Tensor max) |
static T_TensorTensor_T |
torch.aminmax(Tensor self) |
static T_TensorTensor_T |
torch.aminmax(Tensor self,
LongOptional dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.batch_norm_gather_stats_out(Tensor out0,
Tensor out1,
Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
long count) |
static T_TensorTensor_T |
torch.batch_norm_gather_stats_outf(Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
long count,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.batch_norm_gather_stats_with_counts_out(Tensor out0,
Tensor out1,
Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
Tensor counts) |
static T_TensorTensor_T |
torch.batch_norm_gather_stats_with_counts_outf(Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
Tensor counts,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.batch_norm_gather_stats_with_counts(Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
Tensor counts) |
static T_TensorTensor_T |
torch.batch_norm_gather_stats(Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
long count) |
static T_TensorTensor_T |
torch.batch_norm_stats_out(Tensor out0,
Tensor out1,
Tensor input,
double eps) |
static T_TensorTensor_T |
torch.batch_norm_stats_outf(Tensor input,
double eps,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.batch_norm_stats(Tensor input,
double eps) |
static T_TensorTensor_T |
torch.batch_norm_update_stats_out(Tensor out0,
Tensor out1,
Tensor input,
TensorOptional running_mean,
TensorOptional running_var,
double momentum) |
static T_TensorTensor_T |
torch.batch_norm_update_stats_outf(Tensor input,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.batch_norm_update_stats(Tensor input,
TensorOptional running_mean,
TensorOptional running_var,
double momentum) |
static T_TensorTensor_T |
torch.choose_qparams_optimized(Tensor input,
long numel,
long n_bins,
double ratio,
long bit_width) |
static T_TensorTensor_T |
torch.cudnn_grid_sampler_backward_out(Tensor out0,
Tensor out1,
Tensor self,
Tensor grid,
Tensor grad_output) |
static T_TensorTensor_T |
torch.cudnn_grid_sampler_backward_outf(Tensor self,
Tensor grid,
Tensor grad_output,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.cudnn_grid_sampler_backward(Tensor self,
Tensor grid,
Tensor grad_output) |
static T_TensorTensor_T |
torch.cummax_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.cummax_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static T_TensorTensor_T |
torch.cummax_outf(Tensor self,
Dimname dim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.cummax_outf(Tensor self,
long dim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.cummax(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.cummax(Tensor self,
long dim) |
static T_TensorTensor_T |
torch.cummin_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.cummin_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static T_TensorTensor_T |
torch.cummin_outf(Tensor self,
Dimname dim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.cummin_outf(Tensor self,
long dim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.cummin(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.cummin(Tensor self,
long dim) |
static T_TensorTensor_T |
torch.eig(Tensor self) |
static T_TensorTensor_T |
torch.eigh(Tensor self,
BytePointer uplo) |
static T_TensorTensor_T |
torch.eigh(Tensor self,
String uplo) |
static T_TensorTensor_T |
torch.fake_quantize_per_channel_affine_cachemask_out(Tensor out0,
Tensor out1,
Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static T_TensorTensor_T |
torch.fake_quantize_per_channel_affine_cachemask_outf(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.fake_quantize_per_channel_affine_cachemask(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static T_TensorTensor_T |
torch.fake_quantize_per_tensor_affine_cachemask_out(Tensor out0,
Tensor out1,
Tensor self,
double scale,
long zero_point,
long quant_min,
long quant_max) |
static T_TensorTensor_T |
torch.fake_quantize_per_tensor_affine_cachemask_outf(Tensor self,
double scale,
long zero_point,
long quant_min,
long quant_max,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.fake_quantize_per_tensor_affine_cachemask(Tensor self,
double scale,
long zero_point,
long quant_min,
long quant_max) |
static T_TensorTensor_T |
torch.fractional_max_pool2d_out(Tensor output,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool2d_out(Tensor output,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool2d_outf(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static T_TensorTensor_T |
torch.fractional_max_pool2d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static T_TensorTensor_T |
torch.fractional_max_pool2d_with_indices(Tensor input,
FractionalMaxPool2dOptions options)
See the documentation for
/**
torch::nn::functional::FractionalMaxPool2dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.fractional_max_pool2d_with_indices(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool2d(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool3d_out(Tensor output,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool3d_out(Tensor output,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool3d_outf(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static T_TensorTensor_T |
torch.fractional_max_pool3d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static T_TensorTensor_T |
torch.fractional_max_pool3d_with_indices(Tensor input,
FractionalMaxPool3dOptions options)
See the documentation for
/**
torch::nn::functional::FractionalMaxPool3dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.fractional_max_pool3d_with_indices(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool3d(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.fractional_max_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static T_TensorTensor_T |
torch.frexp_out(Tensor mantissa,
Tensor exponent,
Tensor self) |
static T_TensorTensor_T |
torch.frexp_outf(Tensor self,
Tensor mantissa,
Tensor exponent) |
static T_TensorTensor_T |
torch.frexp(Tensor self) |
static T_TensorTensor_T |
torch.geqrf_out(Tensor a,
Tensor tau,
Tensor self) |
static T_TensorTensor_T |
torch.geqrf_outf(Tensor self,
Tensor a,
Tensor tau) |
static T_TensorTensor_T |
torch.geqrf(Tensor self) |
static T_TensorTensor_T |
torch.grid_sampler_2d_backward_out(Tensor out0,
Tensor out1,
Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.grid_sampler_2d_backward_outf(Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners,
BoolPointer output_mask,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.grid_sampler_2d_backward(Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.grid_sampler_3d_backward_out(Tensor out0,
Tensor out1,
Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.grid_sampler_3d_backward_outf(Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners,
BoolPointer output_mask,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.grid_sampler_3d_backward(Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.gru(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.gru(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.gru(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.gru(Tensor input,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self) |
static T_TensorTensor_T |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
long bins,
double[] range,
TensorOptional weight,
boolean density) |
static T_TensorTensor_T |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density) |
static T_TensorTensor_T |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
Tensor bins) |
static T_TensorTensor_T |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
Tensor bins,
TensorOptional weight,
boolean density) |
static T_TensorTensor_T |
torch.histogram_outf(Tensor self,
long bins,
double[] range,
TensorOptional weight,
boolean density,
Tensor hist,
Tensor bin_edges) |
static T_TensorTensor_T |
torch.histogram_outf(Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density,
Tensor hist,
Tensor bin_edges) |
static T_TensorTensor_T |
torch.histogram_outf(Tensor self,
Tensor bins,
TensorOptional weight,
boolean density,
Tensor hist,
Tensor bin_edges) |
static T_TensorTensor_T |
torch.histogram(Tensor self) |
static T_TensorTensor_T |
torch.histogram(Tensor self,
long bins,
double[] range,
TensorOptional weight,
boolean density) |
static T_TensorTensor_T |
torch.histogram(Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density) |
static T_TensorTensor_T |
torch.histogram(Tensor self,
Tensor bins) |
static T_TensorTensor_T |
torch.histogram(Tensor self,
Tensor bins,
TensorOptional weight,
boolean density) |
static T_TensorTensor_T |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k) |
static T_TensorTensor_T |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
Dimname dim) |
static T_TensorTensor_T |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.kthvalue_outf(Tensor self,
long k,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.kthvalue_outf(Tensor self,
long k,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.kthvalue(Tensor self,
long k) |
static T_TensorTensor_T |
torch.kthvalue(Tensor self,
long k,
Dimname dim) |
static T_TensorTensor_T |
torch.kthvalue(Tensor self,
long k,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.kthvalue(Tensor self,
long k,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.linalg_cholesky_ex_out(Tensor L,
Tensor info,
Tensor self) |
static T_TensorTensor_T |
torch.linalg_cholesky_ex_out(Tensor L,
Tensor info,
Tensor self,
boolean upper,
boolean check_errors) |
static T_TensorTensor_T |
torch.linalg_cholesky_ex_outf(Tensor self,
boolean upper,
boolean check_errors,
Tensor L,
Tensor info) |
static T_TensorTensor_T |
torch.linalg_cholesky_ex(Tensor self) |
static T_TensorTensor_T |
torch.linalg_cholesky_ex(Tensor self,
boolean upper,
boolean check_errors) |
static T_TensorTensor_T |
torch.linalg_eig_out(Tensor eigenvalues,
Tensor eigenvectors,
Tensor self) |
static T_TensorTensor_T |
torch.linalg_eig_outf(Tensor self,
Tensor eigenvalues,
Tensor eigenvectors) |
static T_TensorTensor_T |
torch.linalg_eig(Tensor self) |
static T_TensorTensor_T |
torch.linalg_eigh_out(Tensor eigvals,
Tensor eigvecs,
Tensor self) |
static T_TensorTensor_T |
torch.linalg_eigh_out(Tensor eigvals,
Tensor eigvecs,
Tensor self,
BytePointer UPLO) |
static T_TensorTensor_T |
torch.linalg_eigh_out(Tensor eigvals,
Tensor eigvecs,
Tensor self,
String UPLO) |
static T_TensorTensor_T |
torch.linalg_eigh_outf(Tensor self,
BytePointer UPLO,
Tensor eigvals,
Tensor eigvecs) |
static T_TensorTensor_T |
torch.linalg_eigh_outf(Tensor self,
String UPLO,
Tensor eigvals,
Tensor eigvecs) |
static T_TensorTensor_T |
torch.linalg_eigh(Tensor self) |
static T_TensorTensor_T |
torch.linalg_eigh(Tensor self,
BytePointer UPLO) |
static T_TensorTensor_T |
torch.linalg_eigh(Tensor self,
String UPLO) |
static T_TensorTensor_T |
torch.linalg_inv_ex_out(Tensor inverse,
Tensor info,
Tensor A) |
static T_TensorTensor_T |
torch.linalg_inv_ex_out(Tensor inverse,
Tensor info,
Tensor A,
boolean check_errors) |
static T_TensorTensor_T |
torch.linalg_inv_ex_outf(Tensor A,
boolean check_errors,
Tensor inverse,
Tensor info) |
static T_TensorTensor_T |
torch.linalg_inv_ex(Tensor A) |
static T_TensorTensor_T |
torch.linalg_inv_ex(Tensor A,
boolean check_errors) |
static T_TensorTensor_T |
torch.linalg_ldl_factor_out(Tensor LD,
Tensor pivots,
Tensor self) |
static T_TensorTensor_T |
torch.linalg_ldl_factor_out(Tensor LD,
Tensor pivots,
Tensor self,
boolean hermitian) |
static T_TensorTensor_T |
torch.linalg_ldl_factor_outf(Tensor self,
boolean hermitian,
Tensor LD,
Tensor pivots) |
static T_TensorTensor_T |
torch.linalg_ldl_factor(Tensor self) |
static T_TensorTensor_T |
torch.linalg_ldl_factor(Tensor self,
boolean hermitian) |
static T_TensorTensor_T |
torch.linalg_lu_factor_out(Tensor LU,
Tensor pivots,
Tensor A) |
static T_TensorTensor_T |
torch.linalg_lu_factor_out(Tensor LU,
Tensor pivots,
Tensor A,
boolean pivot) |
static T_TensorTensor_T |
torch.linalg_lu_factor_outf(Tensor A,
boolean pivot,
Tensor LU,
Tensor pivots) |
static T_TensorTensor_T |
torch.linalg_lu_factor(Tensor A) |
static T_TensorTensor_T |
torch.linalg_lu_factor(Tensor A,
boolean pivot) |
static T_TensorTensor_T |
torch.linalg_qr_out(Tensor Q,
Tensor R,
Tensor A) |
static T_TensorTensor_T |
torch.linalg_qr_out(Tensor Q,
Tensor R,
Tensor A,
BytePointer mode) |
static T_TensorTensor_T |
torch.linalg_qr_out(Tensor Q,
Tensor R,
Tensor A,
String mode) |
static T_TensorTensor_T |
torch.linalg_qr_outf(Tensor A,
BytePointer mode,
Tensor Q,
Tensor R) |
static T_TensorTensor_T |
torch.linalg_qr_outf(Tensor A,
String mode,
Tensor Q,
Tensor R) |
static T_TensorTensor_T |
torch.linalg_qr(Tensor A) |
static T_TensorTensor_T |
torch.linalg_qr(Tensor A,
BytePointer mode) |
static T_TensorTensor_T |
torch.linalg_qr(Tensor A,
String mode) |
static T_TensorTensor_T |
torch.linalg_slogdet_out(Tensor sign,
Tensor logabsdet,
Tensor A) |
static T_TensorTensor_T |
torch.linalg_slogdet_outf(Tensor A,
Tensor sign,
Tensor logabsdet) |
static T_TensorTensor_T |
torch.linalg_slogdet(Tensor A) |
static T_TensorTensor_T |
torch.linalg_solve_ex_out(Tensor result,
Tensor info,
Tensor A,
Tensor B) |
static T_TensorTensor_T |
torch.linalg_solve_ex_out(Tensor result,
Tensor info,
Tensor A,
Tensor B,
boolean left,
boolean check_errors) |
static T_TensorTensor_T |
torch.linalg_solve_ex_outf(Tensor A,
Tensor B,
boolean left,
boolean check_errors,
Tensor result,
Tensor info) |
static T_TensorTensor_T |
torch.linalg_solve_ex(Tensor A,
Tensor B) |
static T_TensorTensor_T |
torch.linalg_solve_ex(Tensor A,
Tensor B,
boolean left,
boolean check_errors) |
static T_TensorTensor_T |
torch.log_sigmoid_forward_out(Tensor output,
Tensor buffer,
Tensor self) |
static T_TensorTensor_T |
torch.log_sigmoid_forward_outf(Tensor self,
Tensor output,
Tensor buffer) |
static T_TensorTensor_T |
torch.log_sigmoid_forward(Tensor self) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorVector hx,
Tensor w_ih,
Tensor w_hh) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorVector hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static T_TensorTensor_T |
torch.lu_factor(Tensor input)
Computes the LU factorization with partial pivoting
See https://pytorch.org/docs/master/linalg.html#torch.linalg.lu_factor
|
static T_TensorTensor_T |
torch.lu_factor(Tensor self,
boolean pivot) |
static T_TensorTensor_T |
torch.matmul_backward_out(Tensor out0,
Tensor out1,
Tensor grad,
Tensor self,
Tensor other,
BoolPointer mask) |
static T_TensorTensor_T |
torch.matmul_backward_outf(Tensor grad,
Tensor self,
Tensor other,
BoolPointer mask,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.matmul_backward(Tensor grad,
Tensor self,
Tensor other,
BoolPointer mask) |
static T_TensorTensor_T |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
long dim) |
static T_TensorTensor_T |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.max_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor max,
Tensor max_values) |
static T_TensorTensor_T |
torch.max_outf(Tensor self,
long dim,
boolean keepdim,
Tensor max,
Tensor max_values) |
static T_TensorTensor_T |
torch.max_pool1d_with_indices(Tensor self,
long... kernel_size) |
static T_TensorTensor_T |
torch.max_pool1d_with_indices(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool1d_with_indices(Tensor self,
LongArrayRef kernel_size) |
static T_TensorTensor_T |
torch.max_pool1d_with_indices(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool1d_with_indices(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool1d_with_indices(Tensor input,
MaxPool1dOptions options)
See the documentation for
torch::nn::functional::MaxPool1dFuncOptions
/** class to learn what optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long... kernel_size) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices(Tensor self,
long... kernel_size) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices(Tensor self,
LongArrayRef kernel_size) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool2d_with_indices(Tensor input,
MaxPool2dOptions options)
See the documentation for
torch::nn::functional::MaxPool2dFuncOptions
/** class to learn what optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long... kernel_size) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices(Tensor self,
long... kernel_size) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices(Tensor self,
LongArrayRef kernel_size) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static T_TensorTensor_T |
torch.max_pool3d_with_indices(Tensor input,
MaxPool3dOptions options)
See the documentation for
torch::nn::functional::MaxPool3dFuncOptions
/** class to learn what optional arguments are supported for this functional. |
static T_TensorTensor_T |
torch.max(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.max(Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.max(Tensor self,
long dim) |
static T_TensorTensor_T |
torch.max(Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static T_TensorTensor_T |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.median_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.median_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.median(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.median(Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.median(Tensor self,
long dim) |
static T_TensorTensor_T |
torch.median(Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
long dim) |
static T_TensorTensor_T |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.min_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor min,
Tensor min_indices) |
static T_TensorTensor_T |
torch.min_outf(Tensor self,
long dim,
boolean keepdim,
Tensor min,
Tensor min_indices) |
static T_TensorTensor_T |
torch.min(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.min(Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.min(Tensor self,
long dim) |
static T_TensorTensor_T |
torch.min(Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.mkldnn_linear_backward_weights_out(Tensor out0,
Tensor out1,
Tensor grad_output,
Tensor input,
Tensor weight,
boolean bias_defined) |
static T_TensorTensor_T |
torch.mkldnn_linear_backward_weights_outf(Tensor grad_output,
Tensor input,
Tensor weight,
boolean bias_defined,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.mkldnn_linear_backward_weights(Tensor grad_output,
Tensor input,
Tensor weight,
boolean bias_defined) |
static T_TensorTensor_T |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self) |
static T_TensorTensor_T |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.mode_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.mode_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.mode(Tensor self) |
static T_TensorTensor_T |
torch.mode(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.mode(Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.mode(Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.mps_convolution_transpose_backward_out(Tensor out0,
Tensor out1,
Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.mps_convolution_transpose_backward_out(Tensor out0,
Tensor out1,
Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.mps_convolution_transpose_backward_outf(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
BoolPointer output_mask,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.mps_convolution_transpose_backward_outf(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
BoolPointer output_mask,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.mps_convolution_transpose_backward(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.mps_convolution_transpose_backward(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
BoolPointer output_mask) |
static T_TensorTensor_T |
torch.multi_head_attention_forward(Tensor query,
Tensor key,
Tensor value,
long embed_dim_to_check,
long num_heads,
Tensor in_proj_weight,
Tensor in_proj_bias,
Tensor bias_k,
Tensor bias_v,
boolean add_zero_attn,
double dropout_p,
Tensor out_proj_weight,
Tensor out_proj_bias) |
static T_TensorTensor_T |
torch.multi_head_attention_forward(Tensor query,
Tensor key,
Tensor value,
long embed_dim_to_check,
long num_heads,
Tensor in_proj_weight,
Tensor in_proj_bias,
Tensor bias_k,
Tensor bias_v,
boolean add_zero_attn,
double dropout_p,
Tensor out_proj_weight,
Tensor out_proj_bias,
boolean training,
Tensor key_padding_mask,
boolean need_weights,
Tensor attn_mask,
boolean use_separate_proj_weight,
Tensor q_proj_weight,
Tensor k_proj_weight,
Tensor v_proj_weight,
Tensor static_k,
Tensor static_v,
boolean average_attn_weights) |
static T_TensorTensor_T |
torch.multi_head_attention_forward(Tensor query,
Tensor key,
Tensor value,
MultiheadAttentionForwardFuncOptions options) |
static T_TensorTensor_T |
torch.multilabel_margin_loss_forward_out(Tensor output,
Tensor is_target,
Tensor self,
Tensor target,
long reduction) |
static T_TensorTensor_T |
torch.multilabel_margin_loss_forward_outf(Tensor self,
Tensor target,
long reduction,
Tensor output,
Tensor is_target) |
static T_TensorTensor_T |
torch.multilabel_margin_loss_forward(Tensor self,
Tensor target,
long reduction) |
static T_TensorTensor_T |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static T_TensorTensor_T |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.nanmedian_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.nanmedian_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.nanmedian(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.nanmedian(Tensor self,
Dimname dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.nanmedian(Tensor self,
long dim) |
static T_TensorTensor_T |
torch.nanmedian(Tensor self,
long dim,
boolean keepdim) |
static T_TensorTensor_T |
torch.native_dropout_out(Tensor out0,
Tensor out1,
Tensor input,
double p,
BoolOptional train) |
static T_TensorTensor_T |
torch.native_dropout_outf(Tensor input,
double p,
BoolOptional train,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.native_dropout(Tensor input,
double p,
BoolOptional train) |
static T_TensorTensor_T |
torch.nll_loss_forward_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static T_TensorTensor_T |
torch.nll_loss_forward_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor output,
Tensor total_weight) |
static T_TensorTensor_T |
torch.nll_loss_forward_symint_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
SymInt ignore_index) |
static T_TensorTensor_T |
torch.nll_loss_forward_symint_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
SymInt ignore_index,
Tensor output,
Tensor total_weight) |
static T_TensorTensor_T |
torch.nll_loss_forward_symint(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
SymInt ignore_index) |
static T_TensorTensor_T |
torch.nll_loss_forward(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static T_TensorTensor_T |
torch.nll_loss2d_forward_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static T_TensorTensor_T |
torch.nll_loss2d_forward_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor output,
Tensor total_weight) |
static T_TensorTensor_T |
torch.nll_loss2d_forward_symint_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
SymInt ignore_index) |
static T_TensorTensor_T |
torch.nll_loss2d_forward_symint_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
SymInt ignore_index,
Tensor output,
Tensor total_weight) |
static T_TensorTensor_T |
torch.nll_loss2d_forward_symint(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
SymInt ignore_index) |
static T_TensorTensor_T |
torch.nll_loss2d_forward(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static T_TensorTensor_T |
torch.pad_packed_sequence(PackedSequence sequence) |
static T_TensorTensor_T |
torch.pad_packed_sequence(PackedSequence sequence,
boolean batch_first,
double padding_value,
LongOptional total_length)
Pads a packed batch of variable length sequences.
|
static T_TensorTensor_T |
torch.qr_out(Tensor Q,
Tensor R,
Tensor self) |
static T_TensorTensor_T |
torch.qr_out(Tensor Q,
Tensor R,
Tensor self,
boolean some) |
static T_TensorTensor_T |
torch.qr_outf(Tensor self,
boolean some,
Tensor Q,
Tensor R) |
static T_TensorTensor_T |
torch.qr(Tensor self) |
static T_TensorTensor_T |
torch.qr(Tensor self,
boolean some) |
static T_TensorTensor_T |
torch.qr(Tensor input,
BytePointer mode) |
static T_TensorTensor_T |
torch.qr(Tensor input,
String mode) |
static T_TensorTensor_T |
torch.quantized_lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static T_TensorTensor_T |
torch.quantized_lstm_cell(Tensor input,
TensorVector hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static T_TensorTensor_T |
torch.rnn_relu(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.rnn_relu(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.rnn_relu(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.rnn_relu(Tensor input,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.rnn_tanh(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.rnn_tanh(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.rnn_tanh(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.rnn_tanh(Tensor input,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.slogdet_out(Tensor sign,
Tensor logabsdet,
Tensor self) |
static T_TensorTensor_T |
torch.slogdet_outf(Tensor self,
Tensor sign,
Tensor logabsdet) |
static T_TensorTensor_T |
torch.slogdet(Tensor self) |
static T_TensorTensor_T |
torch.solve_ex(Tensor input,
Tensor other,
boolean left,
boolean check_errors) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
Dimname dim) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
long dim,
boolean descending) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean descending) |
static T_TensorTensor_T |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean descending) |
static T_TensorTensor_T |
torch.sort_outf(Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.sort_outf(Tensor self,
BoolOptional stable,
long dim,
boolean descending,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.sort_outf(Tensor self,
Dimname dim,
boolean descending,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.sort_outf(Tensor self,
long dim,
boolean descending,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.sort(Tensor self) |
static T_TensorTensor_T |
torch.sort(Tensor self,
BoolOptional stable) |
static T_TensorTensor_T |
torch.sort(Tensor self,
BoolOptional stable,
Dimname dim) |
static T_TensorTensor_T |
torch.sort(Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending) |
static T_TensorTensor_T |
torch.sort(Tensor self,
BoolOptional stable,
long dim,
boolean descending) |
static T_TensorTensor_T |
torch.sort(Tensor self,
Dimname dim) |
static T_TensorTensor_T |
torch.sort(Tensor self,
Dimname dim,
boolean descending) |
static T_TensorTensor_T |
torch.sort(Tensor self,
long dim,
boolean descending) |
static T_TensorTensor_T |
torch.std_mean_out(Tensor out0,
Tensor out1,
Tensor self) |
static T_TensorTensor_T |
torch.std_mean_out(Tensor out0,
Tensor out1,
Tensor self,
long[] dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean_out(Tensor out0,
Tensor out1,
Tensor self,
LongArrayRefOptional dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean_outf(Tensor self,
long[] dim,
ScalarOptional correction,
boolean keepdim,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.std_mean_outf(Tensor self,
LongArrayRefOptional dim,
ScalarOptional correction,
boolean keepdim,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.std_mean(Tensor self) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
boolean unbiased) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameArrayRef dim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameArrayRef dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameArrayRef dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameVector dim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameVector dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameVector dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
DimnameVector dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
int dim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
long[] dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
long[] dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
LongArrayRefOptional dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
LongArrayRefOptional dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.std_mean(Tensor self,
LongArrayRefOptional dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.topk_out(Tensor values,
Tensor indices,
Tensor self,
long k) |
static T_TensorTensor_T |
torch.topk_out(Tensor values,
Tensor indices,
Tensor self,
long k,
long dim,
boolean largest,
boolean sorted) |
static T_TensorTensor_T |
torch.topk_outf(Tensor self,
long k,
long dim,
boolean largest,
boolean sorted,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.topk_symint_out(Tensor values,
Tensor indices,
Tensor self,
SymInt k) |
static T_TensorTensor_T |
torch.topk_symint_out(Tensor values,
Tensor indices,
Tensor self,
SymInt k,
long dim,
boolean largest,
boolean sorted) |
static T_TensorTensor_T |
torch.topk_symint_outf(Tensor self,
SymInt k,
long dim,
boolean largest,
boolean sorted,
Tensor values,
Tensor indices) |
static T_TensorTensor_T |
torch.topk_symint(Tensor self,
SymInt k) |
static T_TensorTensor_T |
torch.topk_symint(Tensor self,
SymInt k,
long dim,
boolean largest,
boolean sorted) |
static T_TensorTensor_T |
torch.topk(Tensor self,
long k) |
static T_TensorTensor_T |
torch.topk(Tensor self,
long k,
long dim,
boolean largest,
boolean sorted) |
static T_TensorTensor_T |
torch.triangular_solve_out(Tensor X,
Tensor M,
Tensor self,
Tensor A) |
static T_TensorTensor_T |
torch.triangular_solve_out(Tensor X,
Tensor M,
Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular) |
static T_TensorTensor_T |
torch.triangular_solve_outf(Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular,
Tensor X,
Tensor M) |
static T_TensorTensor_T |
torch.triangular_solve(Tensor self,
Tensor A) |
static T_TensorTensor_T |
torch.triangular_solve(Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular) |
static T_TensorTensor_T |
torch.var_mean_out(Tensor out0,
Tensor out1,
Tensor self) |
static T_TensorTensor_T |
torch.var_mean_out(Tensor out0,
Tensor out1,
Tensor self,
long[] dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean_out(Tensor out0,
Tensor out1,
Tensor self,
LongArrayRefOptional dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean_outf(Tensor self,
long[] dim,
ScalarOptional correction,
boolean keepdim,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.var_mean_outf(Tensor self,
LongArrayRefOptional dim,
ScalarOptional correction,
boolean keepdim,
Tensor out0,
Tensor out1) |
static T_TensorTensor_T |
torch.var_mean(Tensor self) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
boolean unbiased) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameArrayRef dim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameArrayRef dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameArrayRef dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameVector dim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameVector dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameVector dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
DimnameVector dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
int dim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
long[] dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
long[] dim,
ScalarOptional correction,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
LongArrayRefOptional dim,
boolean unbiased) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
LongArrayRefOptional dim,
boolean unbiased,
boolean keepdim) |
static T_TensorTensor_T |
torch.var_mean(Tensor self,
LongArrayRefOptional dim,
ScalarOptional correction,
boolean keepdim) |
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