ArtifactMetadataSource source,
            List<ResolutionListener> listeners)
            throws ArtifactResolutionException, ArtifactNotFoundException;

    // USED BY REMOTE RESOURCES PLUGIN, DEPENDENCY PLUGIN, SHADE PLUGIN
    @Deprecated
    void resolve(Artifact artifact, List<ArtifactRepository> remoteRepositories, ArtifactRepository localRepository)
            throws ArtifactResolutionException, ArtifactNotFoundException;

	// the buffer if the stack has been shaded, or even avoid
	// putting them in the buffer at all (which would double its
	// capacity). This is slightly complicated with the buffer; we
	// could track whether any un-shaded goroutine has used the
	// buffer, or just track globally whether there are any
	// un-shaded stacks and flush after each stack scan.
	gcw := &pp.gcw
	pos := 0

// license that can be found in the LICENSE file.

package sha3

// This file defines the ShakeHash interface, and provides
// functions for creating SHAKE and cSHAKE instances, as well as utility
// functions for hashing bytes to arbitrary-length output.
//
//
// SHAKE implementation is based on FIPS PUB 202 [1]
// cSHAKE implementations is based on NIST SP 800-185 [2]
//
// [1] https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.202.pdf

    value {
      list {
        shape {
          dim {
            size: 1
          }
          dim {
            size: 8
          }
        }
        shape {
          dim {
            size: 2
          }
          dim {
            size: -1
          }
          dim {
            size: 16
          }
        }
        shape {
          unknown_rank: true
        }

// Verifies that constants generated post shape inference are propagated.
// get_shape result in this test.
module attributes {tf.versions = {producer = 179 : i32}} {

  // CHECK-LABEL: func @main
  func.func @main(%arg0: tensor<10x19xf32>, %arg1: tensor<19x10xf32> {mhlo.is_same_data_across_replicas = true}) -> tensor<?xi64> {
    %0 = "tf.Shape"(%arg0) : (tensor<10x19xf32>) -> tensor<2xi64>

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-tpu-annotate-dynamic-shape-inputs %s | FileCheck %s

// Test that annotate the inputs of the cluster func to be dynamic shaped.

module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
   func.func @main(
      %arg0: tensor<2048xi64> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"},

Tres Popp <******@****.***> 1648061556 -0700

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   {{.*}})
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key2"]


def Test():

  x = tf.constant(1.0, shape=(3, 3))
  y = tf.constant(1.0, shape=(3, 3))

  s = tf.transpose(x)
  t = tf.transpose(y)

  tensor_info_s = tf.compat.v1.saved_model.utils.build_tensor_info(s)
  tensor_info_t = tf.compat.v1.saved_model.utils.build_tensor_info(t)

Tres Popp <******@****.***> 1648061556 -0700

// Copy the internal data representation of `from` to `to`. `new_dims` and
// `num_new_dims` specify the new shape of the `to` tensor, `type` specifies its
// data type. On success, *status is set to TF_OK and the two tensors share the
// same data buffer.
//
// This call requires that the `from` tensor and the given type and shape (dims
// and num_dims) are "compatible" (i.e. they occupy the same number of bytes).