src/runtime/model.cc (10 lines):
	- line 138: //TODO: consider reduction dim for conv2d and linear
	- line 809: // FIXME: currently create gradients for constants since the current auto grad algorithm
	- line 1076: // FIXME: Currently only support the sample dimension for operators with NCCL
	- line 1466: if(l == metrics_input && metrics_input < (int)layers.size()-1) continue; // TODO: If layer serves for metrics and for further prop
	- line 1556: //TODO: delete fused_op to avoid memory leakage
	- line 1737: // FIXME: currently assume the final layer has exactly one output
	- line 1745: // FIXME: Currently assume 1st input for 1st layer = batch_size
	- line 1834: //TODO: need to make sure opId is not an output layer of the model
	- line 1921: //TODO: need to make sure opId is not an output layer of the model
	- line 2135: // TODO: perform prefetch for performance imporvement


python/flexflow/torch/fx.py (6 lines):
	- line 78: #FIXME assume there is 1 output
	- line 89: #FIXME assume it is a merge
	- line 99: #FIXME may be 3
	- line 156: #FIXME MaxPool2d supports ceil_mode
	- line 167: #FIXME fix kernel, stride and padding
	- line 178: # FIXME BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) args are not in FF


flexflow/torch/fx.py (6 lines):
	- line 78: #FIXME assume there is 1 output
	- line 89: #FIXME assume it is a merge
	- line 99: #FIXME may be 3
	- line 156: #FIXME MaxPool2d supports ceil_mode
	- line 167: #FIXME fix kernel, stride and padding
	- line 178: # FIXME BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) args are not in FF


src/ops/group_by.cu (5 lines):
	- line 49: // FIXME: For now, set upper limits Better: Do as follows, but memory is
	- line 56: assert(_input.numDim == 2); // TODO: support dims > 2
	- line 312: // TODO: why cublas/cudnn stream is needed here?
	- line 369: // TODO: why cublas/cudnn stream is needed here
	- line 499: //TODO: implement


python/flexflow/keras/models/base_model.py (4 lines):
	- line 104: # TODO: finish API
	- line 126: #TODO: finish API
	- line 194: #TODO: finish API
	- line 301: # TODO: move check shape into another function


flexflow/onnx/model.py (4 lines):
	- line 249: # TODO: add cast
	- line 255: # TODO: add unsqueeze
	- line 272: #TODO: this path has not tested
	- line 280: # TODO: add range


python/flexflow/onnx/model.py (4 lines):
	- line 249: # TODO: add cast
	- line 255: # TODO: add unsqueeze
	- line 272: #TODO: this path has not tested
	- line 280: # TODO: add range


python/flexflow_dataloader.cu (4 lines):
	- line 32: //FIXME: currently assume continous indices
	- line 60: //FIXME: currently assume continous indices
	- line 88: //FIXME: currently assume continous indices
	- line 137: //FIXME: currently assume continous indices


python/flexflow_dataloader.cc (4 lines):
	- line 49: // TODO: Use index launcher instead of task launcher
	- line 110: // TODO: Use index launcher instead of task launcher
	- line 356: // TODO: Use index launcher instead of task launcher
	- line 526: // TODO: Use index launcher instead of task launcher


src/mapper/mapper.cc (4 lines):
	- line 364: // FIXME: even though it is a CPU task, we use data parallelism
	- line 458: // TODO: assign priorities
	- line 794: // FIXME: even though it is a CPU task, we use data parallelism
	- line 1210: // FIXME: Legion tracing currently does not support MUST_EPOCH


flexflow/keras/models/base_model.py (4 lines):
	- line 104: # TODO: finish API
	- line 126: #TODO: finish API
	- line 194: #TODO: finish API
	- line 301: # TODO: move check shape into another function


src/ops/aggregate.cu (3 lines):
	- line 40: // FIXME: For now, set upper limits Better: Do as follows, but memory is
	- line 344: // FIXME: These 2 functions could execute independently in parallel
	- line 657: //TODO: implement


src/runtime/model.cu (3 lines):
	- line 345: //TODO: check data type matches
	- line 346: //TODO: Currently we use a task launch, change to index launch for NCCL parameter
	- line 418: //TODO: check data type matches


src/ops/aggregate_spec.cu (2 lines):
	- line 40: // FIXME: For now, set upper limits Better: Do as follows, but memory is
	- line 650: //TODO: implement


python/flexflow/keras_exp/models/model.py (2 lines):
	- line 151: #TODO: finish API
	- line 240: # TODO: move check shape into another function


python/flexflow/keras/layers/core.py (2 lines):
	- line 273: #TODO: target shape does not support -1
	- line 275: # TODO: input shape should not contain batch size for now


scripts/simulator.cc (2 lines):
	- line 584: //FIXME: for now only consider i * j == NUM_PARTITIONS
	- line 653: //FIXME: uncomment us


flexflow/keras_exp/models/model.py (2 lines):
	- line 151: #TODO: finish API
	- line 240: # TODO: move check shape into another function


src/ops/topk.cu (2 lines):
	- line 565: int k = out1_domain.hi()[0] - out1_domain.lo()[0] + 1; /*TODO: This prints to 5*/
	- line 696: // TODO: missing profiling here


src/ops/cache.cu (2 lines):
	- line 248: // TODO: Check why cublas/cudnn stream is needed here
	- line 405: //TODO: implement


src/ops/conv_2d.cu (2 lines):
	- line 108: // TODO: temp work, will let users to pick either NCCL or PS
	- line 674: // TODO: remove this line


src/ops/element_binary.cu (2 lines):
	- line 77: //TODO: implement broadcast op
	- line 115: //TODO: implement broadcast op


flexflow/keras/layers/core.py (2 lines):
	- line 273: #TODO: target shape does not support -1
	- line 275: # TODO: input shape should not contain batch size for now


src/ops/embedding.cc (2 lines):
	- line 353: //FIXME: Not functionaly correct.
	- line 359: // FIXME: Need to be atomic depending on the strategy


src/ops/element_unary.cu (1 line):
	- line 471: //TODO: change to use output instead of recomputing


flexflow/keras/preprocessing/sequence.py (1 line):
	- line 13: _remove_long_seq = sequence._remove_long_seq  # TODO: make it public?


python/flexflow/keras/preprocessing/sequence.py (1 line):
	- line 13: _remove_long_seq = sequence._remove_long_seq  # TODO: make it public?


src/ops/pool_2d.cu (1 line):
	- line 88: //TODO: currently do not support splitting over the channel dimension


flexflow/core/flexflow_cffi.py (1 line):
	- line 1590: #TODO: seperate compute_metrics from backward


src/ops/split.cu (1 line):
	- line 295: //TODO: implement measure_forward


flexflow/torch/nn/modules/module.py (1 line):
	- line 16: # TODO: automatically call this function


python/flexflow_dataloader.h (1 line):
	- line 34: //TODO: remove data loaders except single data loader


src/runtime/initializer_kernel.cu (1 line):
	- line 188: // FIXME: it seems curand has an internal bug with volume < 4


python/flexflow/core/flexflow_cffi.py (1 line):
	- line 1590: #TODO: seperate compute_metrics from backward


nmt/rnn.cu (1 line):
	- line 443: // TODO: to be removed when we have attention layers


src/ops/concat.cu (1 line):
	- line 35: //TODO: swich to use the Legion dim ordering


src/runtime/simulator.cc (1 line):
	- line 419: // TODO add parameter synchronization time


src/ops/batch_matmul.cu (1 line):
	- line 481: // TODO: add support for meta->a_seq_length_dim >= 0


python/flexflow/torch/nn/modules/module.py (1 line):
	- line 16: # TODO: automatically call this function


src/runtime/accessor_kernel.cu (1 line):
	- line 52: // FIXME: currently we zero init the region if not read output


src/ops/linear.cu (1 line):
	- line 633: // TODO: only support relu and sigmoid for now


python/main.cc (1 line):
	- line 95: //TODO: dataloader does not support CR


include/model.h (1 line):
	- line 570: Tensor label_tensor_with_final_part;//FIXME: to be removed