graph(%self : ClassType<JointModel>,
      %input.1 : Float(2, 10, 1024)):
  %1 : ClassType<BiLSTMEncoder> = prim::GetAttr[name="embedding"](%self)
  %2 : ClassType<LSTM> = prim::GetAttr[name="rnn"](%1)
  %3 : Tensor = prim::GetAttr[name="weight_ih_l0"](%2)
  %4 : Tensor = prim::GetAttr[name="weight_hh_l0"](%2)
  %5 : Tensor = prim::GetAttr[name="bias_ih_l0"](%2)
  %6 : Tensor = prim::GetAttr[name="bias_hh_l0"](%2)
  %7 : Tensor = prim::GetAttr[name="weight_ih_l0_reverse"](%2)
  %8 : Tensor = prim::GetAttr[name="weight_hh_l0_reverse"](%2)
  %9 : Tensor = prim::GetAttr[name="bias_ih_l0_reverse"](%2)
  %10 : Tensor = prim::GetAttr[name="bias_hh_l0_reverse"](%2)
  %25 : ClassType<HyperpartisanModel> = prim::GetAttr[name="hyperpartisan_model"](%self)
  %26 : ClassType<SelfAttention> = prim::GetAttr[name="self_attention"](%25)
  %27 : ClassType<Sequential> = prim::GetAttr[name="attention"](%26)
  %28 : ClassType<Linear> = prim::GetAttr[name="0"](%27)
  %weight.2 : Tensor = prim::GetAttr[name="weight"](%28)
  %bias.2 : Tensor = prim::GetAttr[name="bias"](%28)
  %34 : ClassType<SelfAttention> = prim::GetAttr[name="self_attention_sentence"](%25)
  %35 : ClassType<Sequential> = prim::GetAttr[name="attention"](%34)
  %36 : ClassType<Linear> = prim::GetAttr[name="0"](%35)
  %weight.1 : Tensor = prim::GetAttr[name="weight"](%36)
  %bias.1 : Tensor = prim::GetAttr[name="bias"](%36)
  %42 : ClassType<BiLSTMEncoder> = prim::GetAttr[name="doc_embbedding"](%25)
  %43 : ClassType<LSTM> = prim::GetAttr[name="rnn"](%42)
  %44 : Tensor = prim::GetAttr[name="weight_ih_l0"](%43)
  %45 : Tensor = prim::GetAttr[name="weight_hh_l0"](%43)
  %46 : Tensor = prim::GetAttr[name="bias_ih_l0"](%43)
  %47 : Tensor = prim::GetAttr[name="bias_hh_l0"](%43)
  %48 : Tensor = prim::GetAttr[name="weight_ih_l0_reverse"](%43)
  %49 : Tensor = prim::GetAttr[name="weight_hh_l0_reverse"](%43)
  %50 : Tensor = prim::GetAttr[name="bias_ih_l0_reverse"](%43)
  %51 : Tensor = prim::GetAttr[name="bias_hh_l0_reverse"](%43)
  %53 : ClassType<Classifier> = prim::GetAttr[name="doc_classifier"](%25)
  %54 : ClassType<Linear> = prim::GetAttr[name="fcl"](%53)
  %weight : Tensor = prim::GetAttr[name="weight"](%54)
  %bias : Tensor = prim::GetAttr[name="bias"](%54)
  %59 : float = prim::Constant[value=0.1](), scope: JointModel/Dropout # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %60 : bool = prim::Constant[value=0](), scope: JointModel/Dropout # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %tensor.1 : Float(2, 10, 1024) = aten::dropout(%input.1, %59, %60), scope: JointModel/Dropout # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %squeezed_lengths : Long(2) = prim::Constant[value= 10  10 [ Variable[CPULongType]{2} ]](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:67:0
  %63 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:67:0
  %64 : bool = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:67:0
  %65 : Long(2), %permutation_index.1 : Long(2) = aten::sort(%squeezed_lengths, %63, %64), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:67:0
  %67 : int = prim::Constant[value=4](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %68 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %69 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %70 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %71 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %72 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %permutation_index.2 : Long(2) = aten::to(%permutation_index.1, %67, %68, %69, %70, %71, %72), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %74 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:69:0
  %input.2 : Float(2, 10, 1024) = aten::index_select(%tensor.1, %74, %permutation_index.2), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:69:0
  %79 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %80 : int = prim::Constant[value=2](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %81 : int = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %82 : int = prim::Constant[value=4](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %83 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %84 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %85 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %index_range.1 : Long(2) = aten::arange(%79, %80, %81, %82, %83, %84, %85), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %87 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:74:0
  %88 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:74:0
  %89 : Long(2), %reverse_mapping.1 : Long(2) = aten::sort(%permutation_index.2, %87, %88), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:74:0
  %91 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:75:0
  %input_unsort_indices.1 : Long(2) = aten::index_select(%index_range.1, %91, %reverse_mapping.1), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:75:0
  %93 : Long(2) = prim::Constant[value= 10  10 [ Variable[CPULongType]{2} ]](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %94 : Device = prim::Constant[value="cpu"](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %95 : int = prim::Constant[value=4](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %96 : bool = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %97 : bool = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %lengths.1 : Long(2) = aten::to(%93, %94, %95, %96, %97), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %99 : bool = prim::Constant[value=1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:275:0
  %input.3 : Float(20, 1024), %batch_sizes.1 : Long(10) = aten::_pack_padded_sequence(%input.2, %lengths.1, %99), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:275:0
  %105 : int = prim::Constant[value=2](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %106 : int = prim::Constant[value=2](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %107 : int = prim::Constant[value=128](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %108 : int[] = prim::ListConstruct(%105, %106, %107), scope: JointModel/LSTM
  %109 : int = prim::Constant[value=6](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %110 : int = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %111 : Device = prim::Constant[value="cpu"](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %112 : bool = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %hx.1 : Float(2, 2, 128) = aten::zeros(%108, %109, %110, %111, %112), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %152 : Tensor[] = prim::ListConstruct(%hx.1, %hx.1), scope: JointModel/LSTM
  %153 : Tensor[] = prim::ListConstruct(%3, %4, %5, %6, %7, %8, %9, %10), scope: JointModel/LSTM
  %154 : bool = prim::Constant[value=1](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %155 : int = prim::Constant[value=1](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %156 : float = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %157 : bool = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %158 : bool = prim::Constant[value=1](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %159 : Float(20, 256), %160 : Float(2, 2, 128), %161 : Float(2, 2, 128) = aten::lstm(%input.3, %batch_sizes.1, %152, %153, %154, %155, %156, %157, %158), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %162 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:313:0
  %163 : int = aten::size(%batch_sizes.1, %162), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:313:0
  %max_seq_length.1 : Long() = prim::NumToTensor(%163), scope: JointModel
  %165 : int = aten::Int(%max_seq_length.1), scope: JointModel
  %166 : bool = prim::Constant[value=1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:322:0
  %167 : float = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:322:0
  %embedding.1 : Float(2!, 10!, 256), %169 : Long(2) = aten::_pad_packed_sequence(%159, %batch_sizes.1, %166, %167, %165), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:322:0
  %170 : int = prim::Constant[value=4](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %171 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %172 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %173 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %174 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %175 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %input_unsort_indices.2 : Long(2) = aten::to(%input_unsort_indices.1, %170, %171, %172, %173, %174, %175), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %177 : Tensor?[] = prim::ListConstruct(%input_unsort_indices.2), scope: JointModel
  %input.4 : Float(2, 10, 256) = aten::index(%embedding.1, %177), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %183 : Float(256!, 1!) = aten::t(%weight.1), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1371:0
  %output.1 : Float(2, 10, 1) = aten::matmul(%input.4, %183), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1371:0
  %185 : int = prim::Constant[value=1](), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1373:0
  %input.5 : Float(2, 10, 1) = aten::add_(%output.1, %bias.1, %185), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1373:0
  %input.6 : Float(2, 10, 1) = aten::tanh(%input.5), scope: JointModel/Sequential/Tanh[1] # /usr/local/lib/python3.7/site-packages/torch/nn/modules/activation.py:295:0
  %188 : float = prim::Constant[value=0](), scope: JointModel/Sequential/Dropout[2] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %189 : bool = prim::Constant[value=0](), scope: JointModel/Sequential/Dropout[2] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %190 : Float(2, 10, 1) = aten::dropout(%input.6, %188, %189), scope: JointModel/Sequential/Dropout[2] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %input.7 : Float(2, 10) = aten::squeeze(%190), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:63:0
  %192 : int = prim::Constant[value=-1](), scope: JointModel/Softmax # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1230:0
  %193 : int? = prim::Constant(), scope: JointModel/Softmax
  %attentions.1 : Float(2, 10) = aten::softmax(%input.7, %192, %193), scope: JointModel/Softmax # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1230:0
  %206 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %207 : int = aten::size(%attentions.1, %206), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %208 : Long() = prim::NumToTensor(%207), scope: JointModel
  %212 : int = aten::Int(%208), scope: JointModel
  %209 : int = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %210 : int = aten::size(%attentions.1, %209), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %211 : Long() = prim::NumToTensor(%210), scope: JointModel
  %213 : int = aten::Int(%211), scope: JointModel
  %214 : int[] = prim::ListConstruct(%212, %213), scope: JointModel
  %215 : int = prim::Constant[value=6](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %216 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %217 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %218 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %219 : Float(2, 10) = aten::ones(%214, %215, %216, %217, %218), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %mask.1 : Float(2, 10) = aten::detach(%219), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %221 : int = prim::Constant[value=6](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %222 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %223 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %224 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %225 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %226 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %mask.2 : Float(2, 10) = aten::to(%mask.1, %221, %222, %223, %224, %225, %226), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %masked_scores.1 : Float(2, 10) = aten::mul(%attentions.1, %mask.2), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:74:0
  %241 : int = prim::Constant[value=-1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:77:0
  %242 : int[] = prim::ListConstruct(%241), scope: JointModel
  %243 : bool = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:77:0
  %244 : int? = prim::Constant(), scope: JointModel
  %_sums.1 : Float(2, 1) = aten::sum(%masked_scores.1, %242, %243, %244), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:77:0
  %scores.1 : Float(2, 10) = aten::div(%masked_scores.1, %_sums.1), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:78:0
  %247 : int = prim::Constant[value=-1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %248 : Float(2, 10, 1) = aten::unsqueeze(%scores.1, %247), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %249 : Float(2, 10!, 256!) = aten::expand_as(%248, %input.4), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %weighted.1 : Float(2, 10, 256) = aten::mul(%input.4, %249), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %251 : int = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %252 : int[] = prim::ListConstruct(%251), scope: JointModel
  %253 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %254 : int? = prim::Constant(), scope: JointModel
  %255 : Float(2, 256) = aten::sum(%weighted.1, %252, %253, %254), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %256 : Float(2, 256) = aten::squeeze(%255), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %sequence_lengths : Long(1) = prim::Constant[value={2}](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/tensor.py:390:0
  %264 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/tensor.py:390:0
  %265 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/tensor.py:390:0
  %266 : Long() = aten::select(%sequence_lengths, %264, %265), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/tensor.py:390:0
  %267 : int = aten::Int(%266), scope: JointModel
  %268 : int[] = prim::ListConstruct(%267), scope: JointModel
  %269 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/tensor.py:308:0
  %270 : Tensor[] = aten::split_with_sizes(%256, %268, %269), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/tensor.py:308:0
  %s : Float(2, 256) = prim::ListUnpack(%270), scope: JointModel
  %281 : int = prim::Constant[value=1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %282 : int = prim::Constant[value=2](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %283 : int = prim::Constant[value=256](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %284 : int[] = prim::ListConstruct(%281, %282, %283), scope: JointModel
  %285 : int = prim::Constant[value=6](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %286 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %287 : Device = prim::Constant[value="cpu"](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %288 : bool = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %289 : int? = prim::Constant(), scope: JointModel
  %290 : Float(1, 2, 256) = aten::empty(%284, %285, %286, %287, %288, %289), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %291 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %out_tensor : Float(1, 2, 256) = aten::fill_(%290, %291), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:377:0
  %296 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %297 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %298 : Float(2, 256) = aten::select(%out_tensor, %296, %297), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %299 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %300 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %301 : int = prim::Constant[value=2](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %302 : int = prim::Constant[value=1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %303 : Float(2, 256) = aten::slice(%298, %299, %300, %301, %302), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %304 : int = prim::Constant[value=2](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %305 : int = prim::Constant[value=256](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %306 : int[] = prim::ListConstruct(%304, %305), scope: JointModel
  %307 : Float(2, 256) = aten::view(%s, %306), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:382:0
  %308 : bool = prim::Constant[value=0](), scope: JointModel
  %309 : Float(2, 256) = aten::copy_(%303, %307, %308), scope: JointModel
  %310 : float = prim::Constant[value=0](), scope: JointModel/Dropout # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %311 : bool = prim::Constant[value=0](), scope: JointModel/Dropout # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %tensor : Float(1, 2, 256) = aten::dropout(%out_tensor, %310, %311), scope: JointModel/Dropout # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %313 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:67:0
  %314 : bool = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:67:0
  %315 : Long(1), %permutation_index.3 : Long(1) = aten::sort(%sequence_lengths, %313, %314), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:67:0
  %317 : int = prim::Constant[value=4](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %318 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %319 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %320 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %321 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %322 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %permutation_index : Long(1) = aten::to(%permutation_index.3, %317, %318, %319, %320, %321, %322), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:68:0
  %324 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:69:0
  %input.8 : Float(1, 2, 256) = aten::index_select(%tensor, %324, %permutation_index), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:69:0
  %329 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %330 : int = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %331 : int = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %332 : int = prim::Constant[value=4](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %333 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %334 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %335 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %index_range : Long(1) = aten::arange(%329, %330, %331, %332, %333, %334, %335), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:71:0
  %337 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:74:0
  %338 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:74:0
  %339 : Long(1), %reverse_mapping : Long(1) = aten::sort(%permutation_index, %337, %338), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:74:0
  %341 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:75:0
  %input_unsort_indices.3 : Long(1) = aten::index_select(%index_range, %341, %reverse_mapping), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:75:0
  %343 : Long(1) = prim::Constant[value={2}](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %344 : Device = prim::Constant[value="cpu"](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %345 : int = prim::Constant[value=4](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %346 : bool = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %347 : bool = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %lengths : Long(1) = aten::to(%343, %344, %345, %346, %347), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:265:0
  %349 : bool = prim::Constant[value=1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:275:0
  %input.9 : Float(2, 256), %batch_sizes : Long(2) = aten::_pack_padded_sequence(%input.8, %lengths, %349), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:275:0
  %355 : int = prim::Constant[value=2](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %356 : int = prim::Constant[value=1](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %357 : int = prim::Constant[value=128](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %358 : int[] = prim::ListConstruct(%355, %356, %357), scope: JointModel/LSTM
  %359 : int = prim::Constant[value=6](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %360 : int = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %361 : Device = prim::Constant[value="cpu"](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %362 : bool = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %hx : Float(2, 1, 128) = aten::zeros(%358, %359, %360, %361, %362), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:516:0
  %402 : Tensor[] = prim::ListConstruct(%hx, %hx), scope: JointModel/LSTM
  %403 : Tensor[] = prim::ListConstruct(%44, %45, %46, %47, %48, %49, %50, %51), scope: JointModel/LSTM
  %404 : bool = prim::Constant[value=1](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %405 : int = prim::Constant[value=1](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %406 : float = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %407 : bool = prim::Constant[value=0](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %408 : bool = prim::Constant[value=1](), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %409 : Float(2, 256), %410 : Float(2, 1, 128), %411 : Float(2, 1, 128) = aten::lstm(%input.9, %batch_sizes, %402, %403, %404, %405, %406, %407, %408), scope: JointModel/LSTM # /usr/local/lib/python3.7/site-packages/torch/nn/modules/rnn.py:529:0
  %412 : int = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:313:0
  %413 : int = aten::size(%batch_sizes, %412), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:313:0
  %max_seq_length : Long() = prim::NumToTensor(%413), scope: JointModel
  %415 : int = aten::Int(%max_seq_length), scope: JointModel
  %416 : bool = prim::Constant[value=1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:322:0
  %417 : float = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:322:0
  %embedding : Float(1!, 2, 256), %419 : Long(1) = aten::_pad_packed_sequence(%409, %batch_sizes, %416, %417, %415), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/utils/rnn.py:322:0
  %420 : int = prim::Constant[value=4](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %421 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %422 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %423 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %424 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %425 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %input_unsort_indices : Long(1) = aten::to(%input_unsort_indices.3, %420, %421, %422, %423, %424, %425), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %427 : Tensor?[] = prim::ListConstruct(%input_unsort_indices), scope: JointModel
  %input.10 : Float(1, 2, 256) = aten::index(%embedding, %427), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/bilstm_encoder.py:32:0
  %429 : Float(256!, 1!) = aten::t(%weight.2), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1371:0
  %output.2 : Float(1, 2, 1) = aten::matmul(%input.10, %429), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1371:0
  %431 : int = prim::Constant[value=1](), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1373:0
  %input.11 : Float(1, 2, 1) = aten::add_(%output.2, %bias.2, %431), scope: JointModel/Sequential/Linear[0] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1373:0
  %input.12 : Float(1, 2, 1) = aten::tanh(%input.11), scope: JointModel/Sequential/Tanh[1] # /usr/local/lib/python3.7/site-packages/torch/nn/modules/activation.py:295:0
  %434 : float = prim::Constant[value=0](), scope: JointModel/Sequential/Dropout[2] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %435 : bool = prim::Constant[value=0](), scope: JointModel/Sequential/Dropout[2] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %436 : Float(1, 2, 1) = aten::dropout(%input.12, %434, %435), scope: JointModel/Sequential/Dropout[2] # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %input.13 : Float(2) = aten::squeeze(%436), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:63:0
  %438 : int = prim::Constant[value=-1](), scope: JointModel/Softmax # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1230:0
  %439 : int? = prim::Constant(), scope: JointModel/Softmax
  %attentions : Float(2) = aten::softmax(%input.13, %438, %439), scope: JointModel/Softmax # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1230:0
  %448 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %449 : int = aten::size(%attentions, %448), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %450 : Long() = prim::NumToTensor(%449), scope: JointModel
  %451 : int = aten::Int(%450), scope: JointModel
  %452 : int[] = prim::ListConstruct(%451), scope: JointModel
  %453 : int = prim::Constant[value=6](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %454 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %455 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %456 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %457 : Float(2) = aten::ones(%452, %453, %454, %455, %456), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %mask.3 : Float(2) = aten::detach(%457), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:46:0
  %459 : int = prim::Constant[value=6](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %460 : int = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %461 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %462 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %463 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %464 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %mask : Float(2) = aten::to(%mask.3, %459, %460, %461, %462, %463, %464), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:47:0
  %masked_scores : Float(2) = aten::mul(%attentions, %mask), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:74:0
  %475 : int = prim::Constant[value=-1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:77:0
  %476 : int[] = prim::ListConstruct(%475), scope: JointModel
  %477 : bool = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:77:0
  %478 : int? = prim::Constant(), scope: JointModel
  %_sums : Float(1) = aten::sum(%masked_scores, %476, %477, %478), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:77:0
  %scores : Float(2) = aten::div(%masked_scores, %_sums), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:78:0
  %481 : int = prim::Constant[value=-1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %482 : Float(2, 1) = aten::unsqueeze(%scores, %481), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %483 : Float(1, 2!, 256!) = aten::expand_as(%482, %input.10), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %weighted : Float(1, 2, 256) = aten::mul(%input.10, %483), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:85:0
  %485 : int = prim::Constant[value=1](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %486 : int[] = prim::ListConstruct(%485), scope: JointModel
  %487 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %488 : int? = prim::Constant(), scope: JointModel
  %489 : Float(1, 256) = aten::sum(%weighted, %486, %487, %488), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %input.14 : Float(256) = aten::squeeze(%489), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/self_attention.py:88:0
  %491 : float = prim::Constant[value=0.1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %492 : bool = prim::Constant[value=0](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %input.15 : Float(256) = aten::dropout(%input.14, %491, %492), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:806:0
  %494 : Float(256!, 2!) = aten::t(%weight), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1371:0
  %output : Float(2) = aten::matmul(%input.15, %494), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1371:0
  %496 : int = prim::Constant[value=1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1373:0
  %input : Float(2) = aten::add_(%output, %bias, %496), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1373:0
  %498 : int = prim::Constant[value=-1](), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1316:0
  %499 : int? = prim::Constant(), scope: JointModel
  %500 : Float(2) = aten::log_softmax(%input, %498, %499), scope: JointModel # /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:1316:0
  %501 : Long(1) = prim::Constant[value={0}](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/joint_model.py:65:0
  %502 : Device = prim::Constant[value="cpu"](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/joint_model.py:65:0
  %503 : int = prim::Constant[value=4](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/joint_model.py:65:0
  %504 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/joint_model.py:65:0
  %505 : bool = prim::Constant[value=0](), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/joint_model.py:65:0
  %506 : Long(1) = aten::to(%501, %502, %503, %504, %505), scope: JointModel # /Users/perelluis/Documents/Hyperpartisan/hyperpartisan-metaphor-detection/models/joint_model.py:65:0
  %507 : (Long(1), Float(2)) = prim::TupleConstruct(%506, %500)
  return (%507)