SYMBOL INDEX (345 symbols across 27 files)

FILE: datasets/city.py
  function create_dataset (line 21) | def create_dataset(mode="train", class_name="train", expansion=False):
  function load_data (line 42) | def load_data(
  class CityscapesInstances (line 76) | class CityscapesInstances(Dataset):
    method __init__ (line 80) | def __init__(self,
    method _poly2mask (line 168) | def _poly2mask(mask_ann, img_h, img_w):
    method __len__ (line 183) | def __len__(self):
    method __getitem__ (line 186) | def __getitem__(self, item):
  function main (line 196) | def main():

FILE: datasets/monu.py
  function cv2_loader (line 18) | def cv2_loader(path, is_mask):
  function get_monu_transform (line 30) | def get_monu_transform(image_size):
  function create_dataset (line 55) | def create_dataset(mode="train", image_size=256):
  function load_data (line 65) | def load_data(
  class MonuDataset (line 99) | class MonuDataset(torch.utils.data.Dataset):
    method __init__ (line 100) | def __init__(self, root, transform=None, target_transform=None, train=...
    method __getitem__ (line 134) | def __getitem__(self, index):
    method __len__ (line 143) | def __len__(self):

FILE: datasets/preprocess_vaihingen.py
  function get_img (line 10) | def get_img(cfile):
  function get_mask (line 16) | def get_mask(cfile):
  function main (line 24) | def main(args, out_path):

FILE: datasets/transforms.py
  class Compose (line 43) | class Compose(object):
    method __init__ (line 44) | def __init__(self, transforms):
    method __call__ (line 47) | def __call__(self, img, mask):
  class ToTensor (line 53) | class ToTensor(object):
    method __call__ (line 54) | def __call__(self, img, mask):
  class ToPILImage (line 61) | class ToPILImage(object):
    method __init__ (line 62) | def __init__(self, mode=None):
    method __call__ (line 65) | def __call__(self, img, mask):
  class Normalize (line 69) | class Normalize(object):
    method __init__ (line 70) | def __init__(self, mean, std, inplace=False):
    method __call__ (line 75) | def __call__(self, img, mask):
  class Resize (line 79) | class Resize(object):
    method __init__ (line 80) | def __init__(self, size, interpolation=Image.BILINEAR, do_mask=True):
    method __call__ (line 86) | def __call__(self, img, mask):
  class CenterCrop (line 93) | class CenterCrop(object):
    method __init__ (line 94) | def __init__(self, size):
    method __call__ (line 100) | def __call__(self, img, mask):
  class Pad (line 104) | class Pad(object):
    method __init__ (line 105) | def __init__(self, padding, fill=0, padding_mode='constant'):
    method __call__ (line 117) | def __call__(self, img, mask):
  class Lambda (line 122) | class Lambda(object):
    method __init__ (line 123) | def __init__(self, lambd):
    method __call__ (line 127) | def __call__(self, img, mask):
  class Lambda_image (line 131) | class Lambda_image(object):
    method __init__ (line 132) | def __init__(self, lambd):
    method __call__ (line 136) | def __call__(self, img, mask):
  class RandomTransforms (line 140) | class RandomTransforms(object):
    method __init__ (line 141) | def __init__(self, transforms):
    method __call__ (line 145) | def __call__(self, *args, **kwargs):
  class RandomApply (line 149) | class RandomApply(RandomTransforms):
    method __init__ (line 150) | def __init__(self, transforms, p=0.5):
    method __call__ (line 154) | def __call__(self, img, mask):
  class RandomOrder (line 162) | class RandomOrder(RandomTransforms):
    method __call__ (line 163) | def __call__(self, img, mask):
  class RandomChoice (line 171) | class RandomChoice(RandomTransforms):
    method __call__ (line 172) | def __call__(self, img, mask):
  class RandomCrop (line 177) | class RandomCrop(object):
    method __init__ (line 178) | def __init__(self, size, padding=None, pad_if_needed=False, fill=0, pa...
    method get_params (line 189) | def get_params(img, output_size):
    method __call__ (line 199) | def __call__(self, img, mask):
  class RandomHorizontalFlip (line 215) | class RandomHorizontalFlip(object):
    method __init__ (line 216) | def __init__(self, p=0.5):
    method __call__ (line 219) | def __call__(self, img, mask):
  class RandomVerticalFlip (line 225) | class RandomVerticalFlip(object):
    method __init__ (line 226) | def __init__(self, p=0.5):
    method __call__ (line 229) | def __call__(self, img, mask):
  class RandomPerspective (line 235) | class RandomPerspective(object):
    method __init__ (line 236) | def __init__(self, distortion_scale=0.5, p=0.5, interpolation=Image.BI...
    method __call__ (line 241) | def __call__(self, img, mask):
    method get_params (line 253) | def get_params(width, height, distortion_scale):
  class RandomResizedCrop (line 269) | class RandomResizedCrop(object):
    method __init__ (line 270) | def __init__(self, size, mask_size, scale=(0.08, 1.0), ratio=(3. / 4.,...
    method get_params (line 285) | def get_params(img, scale, ratio):
    method __call__ (line 316) | def __call__(self, img, mask):
  class FiveCrop (line 322) | class FiveCrop(object):
    method __init__ (line 323) | def __init__(self, size):
    method __call__ (line 331) | def __call__(self, img, mask):
  class TenCrop (line 335) | class TenCrop(object):
    method __init__ (line 336) | def __init__(self, size, vertical_flip=False):
    method __call__ (line 345) | def __call__(self, img, mask):
  class ColorJitter (line 349) | class ColorJitter(object):
    method __init__ (line 350) | def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
    method _check_input (line 357) | def _check_input(self, value, name, center=1, bound=(0, float('inf')),...
    method get_params (line 377) | def get_params(brightness, contrast, saturation, hue):
    method __call__ (line 401) | def __call__(self, img, mask):
  class RandomRotation (line 407) | class RandomRotation(object):
    method __init__ (line 408) | def __init__(self, degrees, resample=False, expand=False, center=None):
    method get_params (line 423) | def get_params(degrees):
    method __call__ (line 428) | def __call__(self, img, mask):
  class RandomAffine (line 435) | class RandomAffine(object):
    method __init__ (line 436) | def __init__(self, degrees, translate=None, scale=None, shear=None, re...
    method get_params (line 478) | def get_params(degrees, translate, scale_ranges, shears, img_size):
    method __call__ (line 500) | def __call__(self, img, mask):
  class RandomAffineFromSet (line 505) | class RandomAffineFromSet(object):
    method __init__ (line 506) | def __init__(self, degrees, translate=None, scale=None, shear=None, re...
    method get_params (line 543) | def get_params(degrees, translate, scale_ranges, shears, img_size):
    method __call__ (line 565) | def __call__(self, img, mask):

FILE: datasets/vaih.py
  function load_data (line 16) | def load_data(
  class VaihDataset (line 54) | class VaihDataset(Dataset):
    method __init__ (line 60) | def __init__(self, mode, std=np.array([0.22645572 * 255, 0.15276193 * ...
    method __len__ (line 107) | def __len__(self):
    method __getitem__ (line 110) | def __getitem__(self, item):

FILE: image_sample_diff_city.py
  function main (line 27) | def main():
  function create_argparser (line 70) | def create_argparser():

FILE: image_sample_diff_medical.py
  function main (line 27) | def main():
  function create_argparser (line 68) | def create_argparser():

FILE: image_sample_diff_vaih.py
  function main (line 28) | def main():
  function create_argparser (line 72) | def create_argparser():

FILE: image_train_diff_city.py
  function main (line 29) | def main():
  function create_argparser (line 121) | def create_argparser():

FILE: image_train_diff_medical.py
  function main (line 29) | def main():
  function create_argparser (line 119) | def create_argparser():

FILE: image_train_diff_vaih.py
  function main (line 30) | def main():
  function create_argparser (line 120) | def create_argparser():

FILE: improved_diffusion/RRDB.py
  function make_layer (line 7) | def make_layer(block, n_layers):
  class ResidualDenseBlock_5C (line 14) | class ResidualDenseBlock_5C(nn.Module):
    method __init__ (line 15) | def __init__(self, nf=64, gc=32, bias=True):
    method forward (line 28) | def forward(self, x):
  class RRDB (line 37) | class RRDB(nn.Module):
    method __init__ (line 40) | def __init__(self, nf=1, gc=32):
    method forward (line 46) | def forward(self, x):
  class RRDBNet (line 52) | class RRDBNet(nn.Module):
    method __init__ (line 53) | def __init__(self, in_nc=3, out_nc=128, nf=64, nb=3, gc=32):
    method forward (line 65) | def forward(self, x):

FILE: improved_diffusion/dist_util.py
  function setup_dist (line 21) | def setup_dist():
  function dev (line 44) | def dev():
  function load_state_dict (line 53) | def load_state_dict(path, **kwargs):
  function sync_params (line 66) | def sync_params(params):
  function _find_free_port (line 75) | def _find_free_port():

FILE: improved_diffusion/fp16_util.py
  function convert_module_to_f16 (line 9) | def convert_module_to_f16(l):
  function convert_module_to_f32 (line 18) | def convert_module_to_f32(l):
  function make_master_params (line 27) | def make_master_params(model_params):
  function model_grads_to_master_grads (line 40) | def model_grads_to_master_grads(model_params, master_params):
  function master_params_to_model_params (line 50) | def master_params_to_model_params(model_params, master_params):
  function unflatten_master_params (line 64) | def unflatten_master_params(model_params, master_params):
  function zero_grad (line 71) | def zero_grad(model_params):

FILE: improved_diffusion/gaussian_diffusion.py
  function get_named_beta_schedule (line 18) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
  function betas_for_alpha_bar (line 45) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
  class ModelMeanType (line 65) | class ModelMeanType(enum.Enum):
  class ModelVarType (line 75) | class ModelVarType(enum.Enum):
  class LossType (line 89) | class LossType(enum.Enum):
    method is_vb (line 97) | def is_vb(self):
  class GaussianDiffusion (line 101) | class GaussianDiffusion:
    method __init__ (line 118) | def __init__(
    method q_mean_variance (line 171) | def q_mean_variance(self, x_start, t):
    method q_sample (line 188) | def q_sample(self, x_start, t, noise=None):
    method q_posterior_mean_variance (line 208) | def q_posterior_mean_variance(self, x_start, x_t, t):
    method p_mean_variance (line 232) | def p_mean_variance(
    method _predict_xstart_from_eps (line 328) | def _predict_xstart_from_eps(self, x_t, t, eps):
    method _predict_xstart_from_xprev (line 335) | def _predict_xstart_from_xprev(self, x_t, t, xprev):
    method _predict_eps_from_xstart (line 345) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
    method _scale_timesteps (line 351) | def _scale_timesteps(self, t):
    method p_sample (line 356) | def p_sample(
    method p_sample_loop (line 389) | def p_sample_loop(
    method p_sample_loop_progressive (line 431) | def p_sample_loop_progressive(
    method ddim_sample (line 479) | def ddim_sample(
    method ddim_reverse_sample (line 524) | def ddim_reverse_sample(
    method ddim_sample_loop (line 562) | def ddim_sample_loop(
    method ddim_sample_loop_progressive (line 594) | def ddim_sample_loop_progressive(
    method _vb_terms_bpd (line 642) | def _vb_terms_bpd(
    method training_losses (line 677) | def training_losses(self, model, x_start, t, model_kwargs=None, noise=...
    method _prior_bpd (line 753) | def _prior_bpd(self, x_start):
    method calc_bpd_loop (line 771) | def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwar...
  function _extract_into_tensor (line 829) | def _extract_into_tensor(arr, timesteps, broadcast_shape):

FILE: improved_diffusion/image_datasets.py
  function load_data (line 8) | def load_data(
  function _list_image_files_recursively (line 56) | def _list_image_files_recursively(data_dir):
  class ImageDataset (line 68) | class ImageDataset(Dataset):
    method __init__ (line 69) | def __init__(self, resolution, image_paths, classes=None, shard=0, num...
    method __len__ (line 75) | def __len__(self):
    method __getitem__ (line 78) | def __getitem__(self, idx):

FILE: improved_diffusion/logger.py
  class KVWriter (line 26) | class KVWriter(object):
    method writekvs (line 27) | def writekvs(self, kvs):
  class SeqWriter (line 31) | class SeqWriter(object):
    method writeseq (line 32) | def writeseq(self, seq):
  class HumanOutputFormat (line 36) | class HumanOutputFormat(KVWriter, SeqWriter):
    method __init__ (line 37) | def __init__(self, filename_or_file):
    method writekvs (line 48) | def writekvs(self, kvs):
    method _truncate (line 80) | def _truncate(self, s):
    method writeseq (line 84) | def writeseq(self, seq):
    method close (line 93) | def close(self):
  class JSONOutputFormat (line 98) | class JSONOutputFormat(KVWriter):
    method __init__ (line 99) | def __init__(self, filename):
    method writekvs (line 102) | def writekvs(self, kvs):
    method close (line 109) | def close(self):
  class CSVOutputFormat (line 113) | class CSVOutputFormat(KVWriter):
    method __init__ (line 114) | def __init__(self, filename):
    method writekvs (line 119) | def writekvs(self, kvs):
    method close (line 146) | def close(self):
  class TensorBoardOutputFormat (line 150) | class TensorBoardOutputFormat(KVWriter):
    method __init__ (line 155) | def __init__(self, dir):
    method writekvs (line 171) | def writekvs(self, kvs):
    method close (line 185) | def close(self):
  function make_output_format (line 191) | def make_output_format(format, ev_dir, log_suffix=""):
  function logkv (line 212) | def logkv(key, val):
  function logkv_mean (line 221) | def logkv_mean(key, val):
  function logkvs (line 228) | def logkvs(d):
  function dumpkvs (line 236) | def dumpkvs():
  function getkvs (line 243) | def getkvs():
  function log (line 247) | def log(*args, level=INFO):
  function debug (line 254) | def debug(*args):
  function info (line 258) | def info(*args):
  function warn (line 262) | def warn(*args):
  function error (line 266) | def error(*args):
  function set_level (line 270) | def set_level(level):
  function set_comm (line 277) | def set_comm(comm):
  function get_dir (line 281) | def get_dir():
  function profile_kv (line 294) | def profile_kv(scopename):
  function profile (line 303) | def profile(n):
  function get_current (line 325) | def get_current():
  class Logger (line 332) | class Logger(object):
    method __init__ (line 337) | def __init__(self, dir, output_formats, comm=None):
    method logkv (line 347) | def logkv(self, key, val):
    method logkv_mean (line 350) | def logkv_mean(self, key, val):
    method dumpkvs (line 355) | def dumpkvs(self):
    method log (line 376) | def log(self, *args, level=INFO):
    method set_level (line 382) | def set_level(self, level):
    method set_comm (line 385) | def set_comm(self, comm):
    method get_dir (line 388) | def get_dir(self):
    method close (line 391) | def close(self):
    method _do_log (line 397) | def _do_log(self, args):
  function get_rank_without_mpi_import (line 403) | def get_rank_without_mpi_import():
  function mpi_weighted_mean (line 412) | def mpi_weighted_mean(comm, local_name2valcount):
  function configure (line 442) | def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
  function _configure_default_logger (line 474) | def _configure_default_logger():
  function reset (line 479) | def reset():
  function scoped_configure (line 487) | def scoped_configure(dir=None, format_strs=None, comm=None):

FILE: improved_diffusion/losses.py
  function normal_kl (line 12) | def normal_kl(mean1, logvar1, mean2, logvar2):
  function approx_standard_normal_cdf (line 42) | def approx_standard_normal_cdf(x):
  function discretized_gaussian_log_likelihood (line 50) | def discretized_gaussian_log_likelihood(x, *, means, log_scales):

FILE: improved_diffusion/metrics.py
  function WCov_metric (line 5) | def WCov_metric(pred, gt_mask):
  function FBound_metric (line 12) | def FBound_metric(pred, gt_mask):
  function db_eval_boundary (line 21) | def db_eval_boundary(foreground_mask, gt_mask, bound_th):
  function seg2bmap (line 77) | def seg2bmap(seg, width=None, height=None):

FILE: improved_diffusion/nn.py
  class SiLU (line 12) | class SiLU(nn.Module):
    method forward (line 13) | def forward(self, x):
  class GroupNorm32 (line 17) | class GroupNorm32(nn.GroupNorm):
    method forward (line 18) | def forward(self, x):
  function conv_nd (line 22) | def conv_nd(dims, *args, **kwargs):
  function linear (line 35) | def linear(*args, **kwargs):
  function avg_pool_nd (line 42) | def avg_pool_nd(dims, *args, **kwargs):
  function update_ema (line 55) | def update_ema(target_params, source_params, rate=0.99):
  function swap_ema (line 68) | def swap_ema(target_params, source_params):
  function zero_module (line 82) | def zero_module(module):
  function scale_module (line 91) | def scale_module(module, scale):
  function mean_flat (line 100) | def mean_flat(tensor):
  function normalization (line 107) | def normalization(channels):
  function timestep_embedding (line 117) | def timestep_embedding(timesteps, dim, max_period=10000):
  function checkpoint (line 138) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 156) | class CheckpointFunction(th.autograd.Function):
    method forward (line 158) | def forward(ctx, run_function, length, *args):
    method backward (line 167) | def backward(ctx, *output_grads):

FILE: improved_diffusion/resample.py
  function create_named_schedule_sampler (line 8) | def create_named_schedule_sampler(name, diffusion):
  class ScheduleSampler (line 23) | class ScheduleSampler(ABC):
    method weights (line 35) | def weights(self):
    method sample (line 42) | def sample(self, batch_size, device):
  class UniformSampler (line 61) | class UniformSampler(ScheduleSampler):
    method __init__ (line 62) | def __init__(self, diffusion):
    method weights (line 66) | def weights(self):
  class LossAwareSampler (line 70) | class LossAwareSampler(ScheduleSampler):
    method update_with_local_losses (line 71) | def update_with_local_losses(self, local_ts, local_losses):
    method update_with_all_losses (line 107) | def update_with_all_losses(self, ts, losses):
  class LossSecondMomentResampler (line 124) | class LossSecondMomentResampler(LossAwareSampler):
    method __init__ (line 125) | def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
    method weights (line 134) | def weights(self):
    method update_with_all_losses (line 143) | def update_with_all_losses(self, ts, losses):
    method _warmed_up (line 153) | def _warmed_up(self):

FILE: improved_diffusion/respace.py
  function space_timesteps (line 7) | def space_timesteps(num_timesteps, section_counts):
  class SpacedDiffusion (line 63) | class SpacedDiffusion(GaussianDiffusion):
    method __init__ (line 72) | def __init__(self, use_timesteps, **kwargs):
    method p_mean_variance (line 88) | def p_mean_variance(
    method training_losses (line 93) | def training_losses(
    method _wrap_model (line 98) | def _wrap_model(self, model):
    method _scale_timesteps (line 105) | def _scale_timesteps(self, t):
  class _WrappedModel (line 110) | class _WrappedModel:
    method __init__ (line 111) | def __init__(self, model, timestep_map, rescale_timesteps, original_nu...
    method __call__ (line 117) | def __call__(self, x, ts, **kwargs):

FILE: improved_diffusion/sampling_util.py
  function calculate_metrics (line 44) | def calculate_metrics(x, gt):
  function sampling_major_vote_func (line 51) | def sampling_major_vote_func(diffusion_model, ddp_model, output_folder, ...

FILE: improved_diffusion/script_util.py
  function model_and_diffusion_defaults (line 11) | def model_and_diffusion_defaults():
  function create_model_and_diffusion (line 43) | def create_model_and_diffusion(
  function create_model (line 101) | def create_model(
  function sr_model_and_diffusion_defaults (line 151) | def sr_model_and_diffusion_defaults():
  function sr_create_model_and_diffusion (line 162) | def sr_create_model_and_diffusion(
  function sr_create_model (line 214) | def sr_create_model(
  function create_gaussian_diffusion (line 263) | def create_gaussian_diffusion(
  function add_dict_to_argparser (line 304) | def add_dict_to_argparser(parser, default_dict):
  function args_to_dict (line 314) | def args_to_dict(args, keys):
  function str2bool (line 318) | def str2bool(v):

FILE: improved_diffusion/train_util.py
  class TrainLoop (line 34) | class TrainLoop:
    method __init__ (line 35) | def __init__(
    method _load_and_sync_parameters (line 140) | def _load_and_sync_parameters(self, logs_path):
    method _load_ema_parameters (line 163) | def _load_ema_parameters(self, rate, logs_path):
    method _load_optimizer_state (line 179) | def _load_optimizer_state(self, logs_path):
    method _setup_fp16 (line 190) | def _setup_fp16(self):
    method run_loop (line 194) | def run_loop(self, max_iter=250000, start_print_iter=100000, vis_batch...
    method run_step (line 252) | def run_step(self, batch, cond):
    method forward_backward (line 260) | def forward_backward(self, batch, cond):
    method optimize_fp16 (line 300) | def optimize_fp16(self):
    method optimize_normal (line 316) | def optimize_normal(self):
    method _log_grad_norm (line 323) | def _log_grad_norm(self):
    method _anneal_lr (line 329) | def _anneal_lr(self):
    method log_step (line 337) | def log_step(self):
    method save_checkpoint (line 343) | def save_checkpoint(self, rate, params, name):
    method save_state_dict (line 355) | def save_state_dict(self):
    method save (line 391) | def save(self, name):
    method _master_params_to_state_dict (line 408) | def _master_params_to_state_dict(self, master_params):
    method _state_dict_to_master_params (line 419) | def _state_dict_to_master_params(self, state_dict):
  function parse_resume_step_from_filename (line 427) | def parse_resume_step_from_filename(filename):
  function get_blob_logdir (line 442) | def get_blob_logdir():
  function find_resume_checkpoint (line 446) | def find_resume_checkpoint():
  function find_ema_checkpoint (line 452) | def find_ema_checkpoint(main_checkpoint, step, rate):
  function log_loss_dict (line 462) | def log_loss_dict(diffusion, ts, losses):

FILE: improved_diffusion/unet.py
  class TimestepBlock (line 24) | class TimestepBlock(nn.Module):
    method forward (line 30) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 36) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 42) | def forward(self, x, emb):
  class Upsample (line 51) | class Upsample(nn.Module):
    method __init__ (line 61) | def __init__(self, channels, use_conv, dims=2):
    method forward (line 69) | def forward(self, x):
  class Downsample (line 82) | class Downsample(nn.Module):
    method __init__ (line 92) | def __init__(self, channels, use_conv, dims=2):
    method forward (line 103) | def forward(self, x):
  class ResBlock (line 108) | class ResBlock(TimestepBlock):
    method __init__ (line 123) | def __init__(
    method forward (line 173) | def forward(self, x, emb):
    method _forward (line 185) | def _forward(self, x, emb):
  class AttentionBlock (line 201) | class AttentionBlock(nn.Module):
    method __init__ (line 209) | def __init__(self, channels, num_heads=1, use_checkpoint=False):
    method forward (line 220) | def forward(self, x):
    method _forward (line 223) | def _forward(self, x):
  class QKVAttention (line 234) | class QKVAttention(nn.Module):
    method forward (line 239) | def forward(self, qkv):
    method count_flops (line 256) | def count_flops(model, _x, y):
  class UNetModel (line 279) | class UNetModel(nn.Module):
    method __init__ (line 302) | def __init__(
    method convert_to_fp16 (line 441) | def convert_to_fp16(self):
    method convert_to_fp32 (line 450) | def convert_to_fp32(self):
    method inner_dtype (line 460) | def inner_dtype(self):
    method forward (line 466) | def forward(self, x, timesteps, y=None, conditioned_image=None):
    method get_feature_vectors (line 499) | def get_feature_vectors(self, x, timesteps, y=None):
  class SuperResModel (line 532) | class SuperResModel(UNetModel):
    method __init__ (line 539) | def __init__(self, in_channels, *args, **kwargs):
    method forward (line 542) | def forward(self, x, timesteps, low_res=None, **kwargs):
    method get_feature_vectors (line 548) | def get_feature_vectors(self, x, timesteps, low_res=None, **kwargs):

FILE: improved_diffusion/utils.py
  function set_random_seed (line 7) | def set_random_seed(seed, deterministic=False):
  function set_random_seed_for_iterations (line 25) | def set_random_seed_for_iterations(seed):