SYMBOL INDEX (49 symbols across 17 files)

FILE: courses/diffusion-models/diffusion_utilities.py
  class ResidualConvBlock (line 13) | class ResidualConvBlock(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 39) | def forward(self, x: torch.Tensor) -> torch.Tensor:
    method get_out_channels (line 68) | def get_out_channels(self):
    method set_out_channels (line 72) | def set_out_channels(self, out_channels):
  class UnetUp (line 79) | class UnetUp(nn.Module):
    method __init__ (line 80) | def __init__(self, in_channels, out_channels):
    method forward (line 94) | def forward(self, x, skip):
  class UnetDown (line 103) | class UnetDown(nn.Module):
    method __init__ (line 104) | def __init__(self, in_channels, out_channels):
    method forward (line 114) | def forward(self, x):
  class EmbedFC (line 118) | class EmbedFC(nn.Module):
    method __init__ (line 119) | def __init__(self, input_dim, emb_dim):
    method forward (line 137) | def forward(self, x):
  function unorm (line 143) | def unorm(x):
  function norm_all (line 150) | def norm_all(store, n_t, n_s):
  function norm_torch (line 158) | def norm_torch(x_all):
  function gen_tst_context (line 169) | def gen_tst_context(n_cfeat):
  function plot_grid (line 183) | def plot_grid(x,n_sample,n_rows,save_dir,w):
  function plot_sample (line 191) | def plot_sample(x_gen_store,n_sample,nrows,save_dir, fn,  w, save=False):
  class CustomDataset (line 216) | class CustomDataset(Dataset):
    method __init__ (line 217) | def __init__(self, sfilename, lfilename, transform, null_context=False):
    method __len__ (line 228) | def __len__(self):
    method __getitem__ (line 232) | def __getitem__(self, idx):
    method getshapes (line 242) | def getshapes(self):

FILE: courses/genomic-data-science/algorithms-for-dna-sequencing/src/read_genome.py
  function read_genome (line 4) | def read_genome(filename):

FILE: courses/genomic-data-science/algorithms-for-dna-sequencing/src/reverse_complement.py
  function reverse_complement (line 1) | def reverse_complement(s):

FILE: rosalind/cons.py
  function build_matrix (line 175) | def build_matrix(dna_strings):
  function cons (line 193) | def cons():

FILE: rosalind/dna.py
  function test (line 4) | def test(dna):

FILE: rosalind/fib.py
  function rabbit_pairs (line 30) | def rabbit_pairs(n, k):

FILE: rosalind/fibd.py
  function fibd (line 3) | def fibd(n, m):

FILE: rosalind/gc.py
  function parse_fasta (line 3) | def parse_fasta(data):
  function gc_content (line 22) | def gc_content(sequence):
  function highest_gc_content (line 26) | def highest_gc_content(sequences):

FILE: rosalind/hamm.py
  function hamm (line 3) | def hamm(dna_string_1, dna_string_2):

FILE: rosalind/iev.py
  function parse_couples (line 3) | def parse_couples(string):
  function iev (line 6) | def iev(string):
  function iev_with_zip (line 19) | def iev_with_zip(string):

FILE: rosalind/iprb.py
  function iprb (line 3) | def iprb(AA, Aa, aa):

FILE: rosalind/prob.py
  function prob (line 3) | def prob(dna_string, A):

FILE: rosalind/prot.py
  function prot (line 91) | def prot(rna_string):
  function prot_list_comprehension (line 104) | def prot_list_comprehension(rna_string):

FILE: rosalind/prtm.py
  function parse_table (line 28) | def parse_table(monoisotopic_mass_table_string):
  function total_protein_weight (line 37) | def total_protein_weight(monoisotopic_mass_table, sample_dataset):

FILE: rosalind/revc.py
  function revc (line 17) | def revc(dna):

FILE: rosalind/rna.py
  function rna (line 16) | def rna(t):

FILE: rosalind/subs.py
  function subs (line 3) | def subs(s, t):
  function subs_list_comprehension (line 12) | def subs_list_comprehension(s, t):