SYMBOL INDEX (100 symbols across 8 files)

FILE: lfm_torch/liquid_t_moe.py
  class LiquidCell (line 17) | class LiquidCell(nn.Module):
    method __init__ (line 31) | def __init__(
    method forward (line 59) | def forward(self, x: Tensor, h: Tensor) -> Tensor:
    method initialize_hidden_state (line 87) | def initialize_hidden_state(
  class MixtureOfExperts (line 109) | class MixtureOfExperts(nn.Module):
    method __init__ (line 119) | def __init__(
    method forward (line 131) | def forward(self, x: Tensor) -> Tensor:
  class TransformerLayerWithLiquid (line 155) | class TransformerLayerWithLiquid(nn.Module):
    method __init__ (line 166) | def __init__(
    method forward (line 185) | def forward(self, x: Tensor, hidden_state: Tensor) -> Tensor:
  class LiquidTransformer (line 225) | class LiquidTransformer(nn.Module):
    method __init__ (line 237) | def __init__(
    method forward (line 256) | def forward(self, x: Tensor) -> Tensor:

FILE: lfm_torch/model.py
  class AdaptiveLinear (line 8) | class AdaptiveLinear(nn.Module):
    method __init__ (line 13) | def __init__(
    method forward (line 28) | def forward(
  class TokenMixing (line 38) | class TokenMixing(nn.Module):
    method __init__ (line 44) | def __init__(self, token_dim: int, adapt_dim: int):
    method forward (line 50) | def forward(
  class ChannelMixing (line 62) | class ChannelMixing(nn.Module):
    method __init__ (line 68) | def __init__(self, channel_dim: int, adapt_dim: int):
    method forward (line 74) | def forward(
  class MixtureOfExperts (line 81) | class MixtureOfExperts(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 99) | def forward(
  class LFModel (line 110) | class LFModel(nn.Module):
    method __init__ (line 116) | def __init__(
    method forward (line 133) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: lfm_torch/rnn.py
  class LiquidNeuron (line 8) | class LiquidNeuron(nn.Module):
    method __init__ (line 18) | def __init__(
    method forward (line 46) | def forward(
  class LiquidRNN (line 75) | class LiquidRNN(nn.Module):
    method __init__ (line 86) | def __init__(
    method forward (line 118) | def forward(self, x: torch.Tensor) -> torch.Tensor:
    method generate_text (line 147) | def generate_text(
  function cpu_forward_pass_example (line 181) | def cpu_forward_pass_example():

FILE: liquid_transformer_train.py
  class TrainingConfig (line 31) | class TrainingConfig:
  class ArXivDataset (line 64) | class ArXivDataset(IterableDataset):
    method __init__ (line 67) | def __init__(
    method preprocess_text (line 78) | def preprocess_text(self, text: str) -> str:
    method __iter__ (line 82) | def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]:
  class Trainer (line 107) | class Trainer:
    method __init__ (line 110) | def __init__(
    method train_step (line 148) | def train_step(
    method save_checkpoint (line 195) | def save_checkpoint(
    method train (line 216) | def train(
  function main (line 281) | def main():

FILE: research/bench.py
  class ScalingBenchmark (line 9) | class ScalingBenchmark:
    method __init__ (line 10) | def __init__(
    method _generate_input (line 34) | def _generate_input(self, input_size: int) -> torch.Tensor:
    method _test_model (line 44) | def _test_model(
    method run_benchmark (line 81) | def run_benchmark(self) -> None:
    method _categorize_models (line 108) | def _categorize_models(self, performance_data: dict) -> None:

FILE: research/sss_linear.py
  class SparseDynamicLayer (line 11) | class SparseDynamicLayer(nn.Module):
    method __init__ (line 21) | def __init__(
    method forward (line 30) | def forward(self, x: Tensor) -> Tensor:
  class HierarchicalSubstructureLayer (line 52) | class HierarchicalSubstructureLayer(nn.Module):
    method __init__ (line 61) | def __init__(self, input_dim: int):
    method forward (line 66) | def forward(self, x: Tensor) -> Tensor:
  class ProbabilisticMemoryCompressionLayer (line 96) | class ProbabilisticMemoryCompressionLayer(nn.Module):
    method __init__ (line 105) | def __init__(self, input_dim: int, output_dim: int):
    method forward (line 113) | def forward(self, x: Tensor) -> Tensor:
  class SubSubLinearLLM (line 135) | class SubSubLinearLLM(nn.Module):
    method __init__ (line 145) | def __init__(
    method forward (line 158) | def forward(self, x: Tensor) -> Tensor:
  function benchmark_model (line 192) | def benchmark_model(
  function detect_scaling_regime (line 244) | def detect_scaling_regime(
  function plot_benchmark_results (line 269) | def plot_benchmark_results(results: dict, slope: float):

FILE: research/ssub.py
  class EncodingFunction (line 11) | class EncodingFunction(nn.Module):
    method __init__ (line 16) | def __init__(self, input_dim: int, hidden_dim: int):
    method forward (line 20) | def forward(self, x: Tensor) -> Tensor:
  class ImportanceScoring (line 36) | class ImportanceScoring(nn.Module):
    method __init__ (line 41) | def __init__(self, hidden_dim: int):
    method forward (line 45) | def forward(self, x: Tensor) -> Tensor:
  class AggregationFunction (line 61) | class AggregationFunction(nn.Module):
    method __init__ (line 66) | def __init__(self, hidden_dim: int):
    method forward (line 70) | def forward(self, x1: Tensor, x2: Tensor) -> Tensor:
  class OutputFunction (line 90) | class OutputFunction(nn.Module):
    method __init__ (line 95) | def __init__(self, hidden_dim: int, output_dim: int):
    method forward (line 99) | def forward(self, x: Tensor) -> Tensor:
  class HCEN (line 115) | class HCEN(nn.Module):
    method __init__ (line 120) | def __init__(
    method forward (line 134) | def forward(self, x: Tensor) -> Tensor:

FILE: research/sub_linear.py
  class ModelConfig (line 15) | class ModelConfig(BaseModel):
  class SparseInformationExtraction (line 27) | class SparseInformationExtraction(nn.Module):
    method __init__ (line 32) | def __init__(self, input_dim: int, sparsity: float):
    method forward (line 43) | def forward(self, x: Tensor) -> Tensor:
  class HierarchicalClustering (line 73) | class HierarchicalClustering(nn.Module):
    method __init__ (line 78) | def __init__(self, cluster_size: int):
    method forward (line 87) | def forward(self, x: Tensor) -> Tensor:
  class DynamicMaskingActivation (line 106) | class DynamicMaskingActivation(nn.Module):
    method __init__ (line 111) | def __init__(
    method forward (line 129) | def forward(self, x: Tensor) -> Tensor:
  class SparseMemory (line 155) | class SparseMemory(nn.Module):
    method __init__ (line 160) | def __init__(self, input_dim: int, memory_size: int):
    method forward (line 171) | def forward(
  class SDCIModel (line 198) | class SDCIModel(nn.Module):
    method __init__ (line 203) | def __init__(self, config: ModelConfig):
    method forward (line 227) | def forward(
  function benchmark_model (line 278) | def benchmark_model(