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

plexus/plexus-container-default/pom.xml 4.0.0 org.codehaus.plexus plexus-containers 1.0-alpha-32 plexus-container-default Default Plexus Container 1.0-alpha-32 maven-surefire-plugin once **/Test*.java **/Abstract*.java shade-maven-plugin org.codehaus.mojo 1.0-alpha-9 package shade classworlds:classworlds junit:junit jmock:jmock org.codehaus.plexus:plexus-classworlds org.codehaus.plexus:plexus-utils org.codehaus.plexus plexus-component-api org.codehaus.plexus plexus-utils org.codehaus.plexus plexus-classworlds...

plexus/plexus-container-default/pom.xml 4.0.0 org.codehaus.plexus plexus-containers 1.0-alpha-32 plexus-container-default Default Plexus Container 1.0-alpha-32 maven-surefire-plugin once **/Test*.java **/Abstract*.java shade-maven-plugin org.codehaus.mojo 1.0-alpha-9 package shade classworlds:classworlds junit:junit jmock:jmock org.codehaus.plexus:plexus-classworlds org.codehaus.plexus:plexus-utils org.codehaus.plexus plexus-component-api org.codehaus.plexus plexus-utils org.codehaus.plexus plexus-classworlds...

// 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-shape-inference %s | FileCheck %s

module attributes {tf.versions = {producer = 888 : i32}} {
// CHECK-LABEL: testConv2dShapeValidPadding
func.func @testConv2dShapeValidPadding(%arg0: tensor<1x112x80x128xf32>, %arg1: tensor<128x3x3x128xf32>, %arg2: tensor<128xf32>) -> tensor<1x?x?x128xf32> {

// 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).