both are non-zero. If GCDVarTime returns an error, x is not modified. // // The output will be resized to the size of the larger of a and b. func (x *Nat) GCDVarTime(a, b *Nat) (*Nat, error) { u, _, err := extendedGCD(a, b) if err != nil { return nil, err } return x.set(u), nil } // extendedGCD computes u and A such that a = GCD(a, m) and u = A*a - B*m. // // u will have the size of the larger of a and m, and A will have the size of m. // // It is an error if either a or m is zero, or if they are both...

 * whenever there is a chance the check may fail "in real life". Example:
 *
 * {@snippet :
 * Bill bill = remoteService.getLastUnpaidBill();
 *
 * // In case bug 12345 happens again we'd rather just die
 * Verify.verify(bill.status() == Status.UNPAID,
 *     "Unexpected bill status: %s", bill.status());
 * }
 *
 * <h3>Comparison to alternatives</h3>
 *

 * whenever there is a chance the check may fail "in real life". Example:
 *
 * {@snippet :
 * Bill bill = remoteService.getLastUnpaidBill();
 *
 * // In case bug 12345 happens again we'd rather just die
 * Verify.verify(bill.status() == Status.UNPAID,
 *     "Unexpected bill status: %s", bill.status());
 * }
 *
 * <h3>Comparison to alternatives</h3>
 *

`"Will you walk a little faster?" said a whiting to a snail.
"There's a porpoise close behind us, and he's treading on my
 tail.
See how eagerly the lobsters and the turtles all advance!
They are waiting on the shingle--will you come and join the
dance?

Will you, won't you, will you, won't you, will you join the
dance?
Will you, won't you, will you, won't you, won't you join the
dance?

# Requirements for NumPy 1.x
numpy ~= 1.26.0
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

                                                TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. This is only valid on local TFE_TensorHandles. The pointer
// returned will be on the device in which the TFE_TensorHandle resides (so e.g.
// for a GPU tensor this will return a pointer to GPU memory). The pointer is

numpy>=2.2.6 ; python_version == "3.14"
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0 ; python_version <= "3.13"
h5py ~= 3.15.1 ; python_version == "3.14"
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.4, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras

 * acquire(1000)} will result in exactly the same throttling, if any), but it affects the throttling
 * of the <i>next</i> request. I.e., if an expensive task arrives at an idle RateLimiter, it will be
 * granted immediately, but it is the <i>next</i> request that will experience extra throttling,
 * thus paying for the cost of the expensive task.
 *

enum ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  DEVICE_PLACEMENT_WARN = 1,
  // Silently copy the tensor, which has a performance cost since the operation
  // will be blocked till the copy completes. This is the default policy.
  DEVICE_PLACEMENT_SILENT = 2,

                           TF_Status*);

// TF_ExecuteOperation will, if in eager mode, execute, if in graph mode, maybe
// capture some inputs and then add a node in the graph. The output tensors are
// returned through the provided TF_OutputList.
// Any active tape will observe the effects of this execution.
void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,