SYMBOL INDEX (43 symbols across 9 files)

FILE: pytorch/torchsparseattn/base.py
  class _BaseBatchProjection (line 4) | class _BaseBatchProjection(ta.Function):
    method forward (line 7) | def forward(self, x, lengths=None):
    method backward (line 40) | def backward(self, dout):

FILE: pytorch/torchsparseattn/fused.py
  function _inplace_fused_prox_jv_slow (line 22) | def _inplace_fused_prox_jv_slow(y_hat, dout):
  function fused_prox_jv_slow (line 50) | def fused_prox_jv_slow(y_hat, dout):
  function fused_prox_jv_fast (line 56) | def fused_prox_jv_fast(y_hat, dout):
  class FusedProxFunction (line 62) | class FusedProxFunction(_BaseBatchProjection):
    method __init__ (line 64) | def __init__(self, alpha=1):
    method project (line 67) | def project(self, x):
    method project_jv (line 73) | def project_jv(self, dout, y_hat):
  class Fusedmax (line 79) | class Fusedmax(nn.Module):
    method __init__ (line 80) | def __init__(self, alpha=1):
    method forward (line 84) | def forward(self, x, lengths=None):

FILE: pytorch/torchsparseattn/isotonic.py
  function isotonic_regression (line 12) | def isotonic_regression(y, sample_weight=None, y_min=None, y_max=None,

FILE: pytorch/torchsparseattn/oscar.py
  function oscar_prox_jv (line 20) | def oscar_prox_jv(y_hat, dout):
  function prox_owl (line 40) | def prox_owl(v, w):
  function _oscar_weights (line 65) | def _oscar_weights(alpha, beta, size):
  class OscarProxFunction (line 72) | class OscarProxFunction(_BaseBatchProjection):
    method __init__ (line 91) | def __init__(self, alpha=0, beta=1):
    method project (line 95) | def project(self, x):
    method project_jv (line 102) | def project_jv(self, dout, y_hat):
  class Oscarmax (line 106) | class Oscarmax(nn.Module):
    method __init__ (line 107) | def __init__(self, beta=1):
    method forward (line 111) | def forward(self, x, lengths=None):
  function _run_backward (line 126) | def _run_backward(x):

FILE: pytorch/torchsparseattn/sparsemax.py
  function project_simplex (line 17) | def project_simplex(v, z=1):
  function sparsemax_grad (line 28) | def sparsemax_grad(dout, w_star):
  class SparsemaxFunction (line 38) | class SparsemaxFunction(_BaseBatchProjection):
    method project (line 40) | def project(self, x):
    method project_jv (line 43) | def project_jv(self, dout, y_star):
  class Sparsemax (line 47) | class Sparsemax(nn.Module):
    method forward (line 49) | def forward(self, x, lengths=None):

FILE: pytorch/torchsparseattn/test_attention.py
  class AttentionRegressor (line 10) | class AttentionRegressor(nn.Module):
    method __init__ (line 12) | def __init__(self, projection, n_features=100):
    method forward (line 18) | def forward(self, X, lengths):
  function test_attention (line 32) | def test_attention(projection):

FILE: pytorch/torchsparseattn/test_fused.py
  function _fused_prox_jacobian (line 12) | def _fused_prox_jacobian(y_hat, dout=None):
  function test_jv (line 39) | def test_jv(alpha):
  function test_finite_diff (line 59) | def test_finite_diff(alpha):

FILE: pytorch/torchsparseattn/test_oscar.py
  function _oscar_prox_jacobian (line 12) | def _oscar_prox_jacobian(y_star, dout=None):
  function test_jv (line 35) | def test_jv(alpha, beta):
  function test_finite_diff (line 52) | def test_finite_diff(alpha, beta):

FILE: pytorch/torchsparseattn/test_sparsemax.py
  function test_sparsemax (line 6) | def test_sparsemax():