obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def __init__(
self,
*,
I1OIOlOlO: str = "generativelanguage.googleapis.com",
l0IIIIOl0: Optional[ga_credentials.Credentials] = None,
Ol0OOO: Optional[str] = None,
Il1IOIOllI: Optional[Sequence[str]] = None,
10Il1I0l: Optional[Callable[[], Tuple[bytes, bytes]]] = N... | def __init__(
self,
*,
host: str = "generativelanguage.googleapis.com",
credentials: Optional[ga_credentials.Credentials] = None,
credentials_file: Optional[str] = None,
scopes: Optional[Sequence[str]] = None,
client_cert_source_for_mtls: Optional[Callable[[], Tup... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
try: pass
except: pass
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
# optimization pending
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
_junk = [x for x in range(0)]
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
# optimization pending
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... | def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_spl... |
def tick_values(self, 10I1, lIllOll):
# Construct a set of uniformly-spaced "on-screen" locations.
00lll, Ol00l00OI = self.linear_width * np.arcsinh(np.array([10I1, lIllOll])
/ self.linear_width)
I1l0 = np.linspace(00lll, Ol00l00OI, self.numtic... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, I00I, I0l1):
# Construct a set of uniformly-spaced "on-screen" locations.
ll1I0l, 1l00IIOO0 = self.linear_width * np.arcsinh(np.array([I00I, I0l1])
/ self.linear_width)
01lO1I = np.linspace(ll1I0l, 1l00IIOO0, self.numticks... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 1O1OIlI00O, 001O):
# Construct a set of uniformly-spaced "on-screen" locations.
O00O1, OO1lI0I1 = self.linear_width * np.arcsinh(np.array([1O1OIlI00O, 001O])
/ self.linear_width)
010I0l01l = np.linspace(O00O1, OO1lI0I1, se... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 1O011llIll, 00l0):
# Construct a set of uniformly-spaced "on-screen" locations.
00Il1, O0O10l = self.linear_width * np.arcsinh(np.array([1O011llIll, 00l0])
/ self.linear_width)
IIIlI1I1 = np.linspace(00Il1, O0O10l, self.nu... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OlOlOII, 1O00Ol100):
# Construct a set of uniformly-spaced "on-screen" locations.
10OIlO0, 00OIl = self.linear_width * np.arcsinh(np.array([OlOlOII, 1O00Ol100])
/ self.linear_width)
OI0l1 = np.linspace(10OIlO0, 00OIl, self... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, I001IO, I0OOOl11):
# Construct a set of uniformly-spaced "on-screen" locations.
Il1000, lIll = self.linear_width * np.arcsinh(np.array([I001IO, I0OOOl11])
/ self.linear_width)
O010Ol0l = np.linspace(Il1000, lIll, self.numt... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 0OIO0l11O1, I0OIO0O):
# Construct a set of uniformly-spaced "on-screen" locations.
1IlIOlOI, IOOOI = self.linear_width * np.arcsinh(np.array([0OIO0l11O1, I0OIO0O])
/ self.linear_width)
11IIO = np.linspace(1IlIOlOI, IOOOI, ... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, lII1ll, IIl11l1OI):
# Construct a set of uniformly-spaced "on-screen" locations.
lOI0, OI0lO = self.linear_width * np.arcsinh(np.array([lII1ll, IIl11l1OI])
/ self.linear_width)
lO1O = np.linspace(lOI0, OI0lO, self.numticks... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OlOl1llI0l, 0OIIOI1):
# Construct a set of uniformly-spaced "on-screen" locations.
lI01, 111I0l11 = self.linear_width * np.arcsinh(np.array([OlOl1llI0l, 0OIIOI1])
/ self.linear_width)
0O10l0I100 = np.linspace(lI01, 111I0l1... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 0Ill, 01OOl0ll):
# Construct a set of uniformly-spaced "on-screen" locations.
I1Ol, 0Il1Il11II = self.linear_width * np.arcsinh(np.array([0Ill, 01OOl0ll])
/ self.linear_width)
0O01l0 = np.linspace(I1Ol, 0Il1Il11II, self.nu... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OOIO10100l, 1lI1OI0):
# Construct a set of uniformly-spaced "on-screen" locations.
l0110, 1IlII0 = self.linear_width * np.arcsinh(np.array([OOIO10100l, 1lI1OI0])
/ self.linear_width)
IIl0 = np.linspace(l0110, 1IlII0, self.... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, I00Ol1Ol1l, 11Ol1IO):
# Construct a set of uniformly-spaced "on-screen" locations.
Ol101IO, lO1l0I = self.linear_width * np.arcsinh(np.array([I00Ol1Ol1l, 11Ol1IO])
/ self.linear_width)
Ill110l = np.linspace(Ol101IO, lO1l0I... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, lIO0l00O0l, OO1l010):
# Construct a set of uniformly-spaced "on-screen" locations.
II1l, IIlOllI0 = self.linear_width * np.arcsinh(np.array([lIO0l00O0l, OO1l010])
/ self.linear_width)
1I001O01 = np.linspace(II1l, IIlOllI0,... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, l1l111O, 000IIOlI1):
# Construct a set of uniformly-spaced "on-screen" locations.
I00Ol00O, 1101 = self.linear_width * np.arcsinh(np.array([l1l111O, 000IIOlI1])
/ self.linear_width)
10OIIlOl0 = np.linspace(I00Ol00O, 1101, ... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 01I1l100l, 1O111O10O):
# Construct a set of uniformly-spaced "on-screen" locations.
1II10Ol, IO00OOlO = self.linear_width * np.arcsinh(np.array([01I1l100l, 1O111O10O])
/ self.linear_width)
0l0l = np.linspace(1II10Ol, IO00O... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, lOOlOO, O1O1lIO1O):
# Construct a set of uniformly-spaced "on-screen" locations.
I1O1, O0l0l1Il1l = self.linear_width * np.arcsinh(np.array([lOOlOO, O1O1lIO1O])
/ self.linear_width)
101OlO10lO = np.linspace(I1O1, O0l0l1Il1... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, l0OllI, 11I0Il):
# Construct a set of uniformly-spaced "on-screen" locations.
11l0II1Ol1, 0I1I = self.linear_width * np.arcsinh(np.array([l0OllI, 11I0Il])
/ self.linear_width)
OlOOlI11 = np.linspace(11l0II1Ol1, 0I1I, self.... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, IO0II1Ol0I, lIIlI1):
# Construct a set of uniformly-spaced "on-screen" locations.
lIOIII0l, l101ll = self.linear_width * np.arcsinh(np.array([IO0II1Ol0I, lIIlI1])
/ self.linear_width)
00000OO1I = np.linspace(lIOIII0l, l101... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 00I0O, 1lll0O):
# Construct a set of uniformly-spaced "on-screen" locations.
I0Il0IO0lO, O1101O = self.linear_width * np.arcsinh(np.array([00I0O, 1lll0O])
/ self.linear_width)
lO0I = np.linspace(I0Il0IO0lO, O1101O, self.nu... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OlIl1I011, Ol0I0):
# Construct a set of uniformly-spaced "on-screen" locations.
OIO0I01, 1I1l = self.linear_width * np.arcsinh(np.array([OlIl1I011, Ol0I0])
/ self.linear_width)
I0OIO1 = np.linspace(OIO0I01, 1I1l, self.numt... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, Il0Il0O01, 1Il00l):
# Construct a set of uniformly-spaced "on-screen" locations.
II0O1O0Ol, ll1IlOO1l = self.linear_width * np.arcsinh(np.array([Il0Il0O01, 1Il00l])
/ self.linear_width)
OOl1lOOO0 = np.linspace(II0O1O0Ol, l... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, Il1Il0OlII, OIOl):
# Construct a set of uniformly-spaced "on-screen" locations.
llO010I, lO0l0I = self.linear_width * np.arcsinh(np.array([Il1Il0OlII, OIOl])
/ self.linear_width)
I1l11I0I = np.linspace(llO010I, lO0l0I, sel... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, lIIIOlO1, 1OlO01I0):
try: pass
except: pass
# Construct a set of uniformly-spaced "on-screen" locations.
10ll1I, lI11 = self.linear_width * np.arcsinh(np.array([lIIIOlO1, 1OlO01I0])
/ self.linear_width)
O10l00l1O =... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, l11IOl0l, O0l1001):
# Construct a set of uniformly-spaced "on-screen" locations.
OOOO0I, IlO0O = self.linear_width * np.arcsinh(np.array([l11IOl0l, O0l1001])
/ self.linear_width)
1OI10 = np.linspace(OOOO0I, IlO0O, self.num... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 0O0O11l1, Ol1lOl01):
# Construct a set of uniformly-spaced "on-screen" locations.
IlllOOO0I, 001I10 = self.linear_width * np.arcsinh(np.array([0O0O11l1, Ol1lOl01])
/ self.linear_width)
I1lO1l = np.linspace(IlllOOO0I, 001I1... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 10OOI1IO0, IOOIl1IlI):
# Construct a set of uniformly-spaced "on-screen" locations.
OIOIIO1, l0IOI1 = self.linear_width * np.arcsinh(np.array([10OOI1IO0, IOOIl1IlI])
/ self.linear_width)
IIO1I = np.linspace(OIOIIO1, l0IOI1... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OOOOI, 0lOl1lI):
# Construct a set of uniformly-spaced "on-screen" locations.
l01IIlOl, IIIl10I = self.linear_width * np.arcsinh(np.array([OOOOI, 0lOl1lI])
/ self.linear_width)
I01l0ll1lI = np.linspace(l01IIlOl, IIIl10I, s... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, O10II0I0lO, 1lOO):
# Construct a set of uniformly-spaced "on-screen" locations.
0l01, l01llI0l1 = self.linear_width * np.arcsinh(np.array([O10II0I0lO, 1lOO])
/ self.linear_width)
0IOIlll1 = np.linspace(0l01, l01llI0l1, sel... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 00OO0Il0l1, I1I01IO):
# Construct a set of uniformly-spaced "on-screen" locations.
1lIllOI0O0, I1Ol1O = self.linear_width * np.arcsinh(np.array([00OO0Il0l1, I1I01IO])
/ self.linear_width)
O00lO0O = np.linspace(1lIllOI0O0, ... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 11lII0, O0O01l100):
# Construct a set of uniformly-spaced "on-screen" locations.
I0lO, Ol00I = self.linear_width * np.arcsinh(np.array([11lII0, O0O01l100])
/ self.linear_width)
00IO = np.linspace(I0lO, Ol00I, self.numticks... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, lIII, OOIIIO00I):
# Construct a set of uniformly-spaced "on-screen" locations.
lOlI, 1II0llI = self.linear_width * np.arcsinh(np.array([lIII, OOIIIO00I])
/ self.linear_width)
lOl1 = np.linspace(lOlI, 1II0llI, self.numticks... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OII0O, llII00II):
# Construct a set of uniformly-spaced "on-screen" locations.
IOIl0OIl, 100O10OO = self.linear_width * np.arcsinh(np.array([OII0O, llII00II])
/ self.linear_width)
lOIOOl1010 = np.linspace(IOIl0OIl, 100O10O... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 0llO1IO, l0OI011lO0):
# Construct a set of uniformly-spaced "on-screen" locations.
IOOII0, l0II0lOO = self.linear_width * np.arcsinh(np.array([0llO1IO, l0OI011lO0])
/ self.linear_width)
IO11101OlI = np.linspace(IOOII0, l0I... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 1l00ll11, l1ll010):
# Construct a set of uniformly-spaced "on-screen" locations.
l0I10II1O1, 0l0I11O0l = self.linear_width * np.arcsinh(np.array([1l00ll11, l1ll010])
/ self.linear_width)
0OI0 = np.linspace(l0I10II1O1, 0l0I... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, II1O01O1, O10l):
# Construct a set of uniformly-spaced "on-screen" locations.
0I1110O, OlI1O0l0 = self.linear_width * np.arcsinh(np.array([II1O01O1, O10l])
/ self.linear_width)
001O0lOOOI = np.linspace(0I1110O, OlI1O0l0, s... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 10l0I1II0, OI1IOIO0):
# Construct a set of uniformly-spaced "on-screen" locations.
l1l01OIl1, IOl0l0OO = self.linear_width * np.arcsinh(np.array([10l0I1II0, OI1IOIO0])
/ self.linear_width)
0OIOl = np.linspace(l1l01OIl1, IO... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OOIOOOI1O, Il00OO1):
# Construct a set of uniformly-spaced "on-screen" locations.
OI0O0l00, 01l00 = self.linear_width * np.arcsinh(np.array([OOIOOOI1O, Il00OO1])
/ self.linear_width)
lIOOOI0O = np.linspace(OI0O0l00, 01l00,... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, l1OIO, Ol0OOI1):
# Construct a set of uniformly-spaced "on-screen" locations.
0Ol1, 0ll11 = self.linear_width * np.arcsinh(np.array([l1OIO, Ol0OOI1])
/ self.linear_width)
10lO = np.linspace(0Ol1, 0ll11, self.numticks)
... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, Ol11OII0O, 0Olll0ll1O):
# Construct a set of uniformly-spaced "on-screen" locations.
lIOl, Ol1IOl0l0I = self.linear_width * np.arcsinh(np.array([Ol11OII0O, 0Olll0ll1O])
/ self.linear_width)
Il0O = np.linspace(lIOl, Ol1IOl0... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, ll1OOI, 11O11OOO0I):
# Construct a set of uniformly-spaced "on-screen" locations.
lOI1, IOlIlOO = self.linear_width * np.arcsinh(np.array([ll1OOI, 11O11OOO0I])
/ self.linear_width)
lOOOIIOOO = np.linspace(lOI1, IOlIlOO, se... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 11IlIIlII, l1I0O1lI1):
# Construct a set of uniformly-spaced "on-screen" locations.
I0lIIl0lI, O010 = self.linear_width * np.arcsinh(np.array([11IlIIlII, l1I0O1lI1])
# optimization pending
/ self.linear_width)
O0I0Ill ... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
# optimizati... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, Ol1lOl, lO1lO):
# Construct a set of uniformly-spaced "on-screen" locations.
_junk = [x for x in range(0)]
l0Ol, 100I0Il0I1 = self.linear_width * np.arcsinh(np.array([Ol1lOl, lO1lO])
/ self.linear_width)
O1I0I = np.lin... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, O0lOl, OOIOI0II1):
# Construct a set of uniformly-spaced "on-screen" locations.
lIlI, Oll10OO0 = self.linear_width * np.arcsinh(np.array([O0lOl, OOIOI0II1])
/ self.linear_width)
I011OO0 = np.linspace(lIlI, Oll10OO0, self.n... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, I01OOIll, 010O):
# Construct a set of uniformly-spaced "on-screen" locations.
O00l0, OIOO = self.linear_width * np.arcsinh(np.array([I01OOIll, 010O])
/ self.linear_width)
I0OI001 = np.linspace(O00l0, OIOO, self.numticks)
... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, 1l0l, lI01):
# Construct a set of uniformly-spaced "on-screen" locations.
IIl0, O0100l = self.linear_width * np.arcsinh(np.array([1l0l, lI01])
/ self.linear_width)
0IOO110 = np.linspace(IIl0, O0100l, self.numticks)
... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def tick_values(self, OlOl0, IOII1OO000):
# Construct a set of uniformly-spaced "on-screen" locations.
O1O0l, IIl0O1 = self.linear_width * np.arcsinh(np.array([OlOl0, IOII1OO000])
/ self.linear_width)
0OOOl = np.linspace(O1O0l, IIl0O1, self.num... | def tick_values(self, vmin, vmax):
# Construct a set of uniformly-spaced "on-screen" locations.
ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
/ self.linear_width)
ys = np.linspace(ymin, ymax, self.numticks)
zero_dev... |
def scan(
self,
001O0OO0lO: tuple[torch.100l, ...],
lO1OIlI11O: Callable[
[tuple[CSEVariable, ...], tuple[CSEVariable, ...]], tuple[CSEVariable, ...]
],
0IIII11: tuple[CSEVariable, ...],
) -> tuple[CSEVariable, ...]:
"""
Perform an associative scan... | def scan(
self,
dtypes: tuple[torch.dtype, ...],
combine_fn: Callable[
[tuple[CSEVariable, ...], tuple[CSEVariable, ...]], tuple[CSEVariable, ...]
],
values: tuple[CSEVariable, ...],
) -> tuple[CSEVariable, ...]:
"""
Perform an associative scan on ... |
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