TF_CHECK_OK(MessageToBuffer(node_in, buffer));
  TF_CHECK_OK(BufferToMessage(buffer, &node_out));
  TF_DeleteBuffer(buffer);

  protobuf::util::MessageDifferencer differencer;
  EXPECT_TRUE(differencer.Compare(node_in, node_out));
}

TEST(CAPI, TestTensorNonScalarBytesAllocateDelete) {
  const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];

    }
    status->status = desc->node_builder.Finalize(&desc->graph->graph, &ret,
                                                 /*consume=*/true);

    if (status->status.ok()) {
      // Run shape inference function for newly added node.
      status->status = desc->graph->refiner.AddNode(ret);
    }
    if (status->status.ok()) {
      // Add the node to the name-to-node mapping.

   *
   *  * Invalid percent-encoded sequences like `%xx` will be encoded like `%25xx`.
   *
   *  * Whitespace and control characters in the fragment will be stripped.
   *
   * These differences may have a significant consequence when the URI is interpreted by a
   * web server. For this reason the [URI class][URI] and this method should be avoided.
   */
  @JvmName("uri")
  fun toUri(): URI {

license that can be found in the LICENSE file. //go:build !purego #include "textflag.h" // This is a port of the s390x asm implementation. // to ppc64le. // Some changes were needed due to differences in // the Go opcodes and/or available instructions // between s390x and ppc64le. // 1. There were operand order differences in the // VSUBUQM, VSUBCUQ, and VSEL instructions. // 2. ppc64 does not have a multiply high and low // like s390x, so those were implemented using // macros to compute the equivalent...

$enable-responsive-font-sizes {\n      font-size: #{$rfs-static}#{$rfs-suffix};\n    }\n    @else {\n      // Calculate the minimum font size for $fs\n      $fs-min: $rfs-base-font-size + divide($fs - $rfs-base-font-size, $rfs-factor);\n\n      // Calculate difference between $fs and the minimum font size\n      $fs-diff: $fs - $fs-min;\n\n      // Base font-size formatting\n      $min-width: if($rfs-font-size-unit == rem, #{divide($fs-min, $rfs-rem-value)}rem, #{$fs-min}px);\n\n      // Use `vmin` if two-dimensional...

  }

  @SuppressWarnings("JUnitIncompatibleType")
  public void testWhere() {
    assertEquals(new TypeToken<Map<String, Integer>>() {}, mapOf(String.class, Integer.class));
    // Type inference is doomed here: int.class is the same as Integer.class, so this is comparing
    // TypeToken<int[]> and TypeToken<Integer[]>.
    assertEquals(new TypeToken<int[]>() {}, arrayOf(int.class));

TF_CAPI_EXPORT extern void TF_RemoveAllControlInputs(TF_Graph* graph,
                                                     TF_Operation* op);

// Set if `graph` requires shape inference functions.
TF_CAPI_EXPORT extern void TF_SetRequireShapeInferenceFns(TF_Graph* graph,
                                                          bool require);

  }

  @SuppressWarnings("JUnitIncompatibleType")
  public void testWhere() {
    assertEquals(new TypeToken<Map<String, Integer>>() {}, mapOf(String.class, Integer.class));
    // Type inference is doomed here: int.class is the same as Integer.class, so this is comparing
    // TypeToken<int[]> and TypeToken<Integer[]>.
    assertEquals(new TypeToken<int[]>() {}, arrayOf(int.class));

				maxUsedPct = pctUsed
			}
		}

		// Since we are comparing pools that may have a different number of sets
		// we multiply by the number of sets in the pool.
		// This will compensate for differences in set sizes
		// when choosing destination pool.
		// Different set sizes are already compensated by less disks.
		available *= uint64(nSets[i])

		serverPools[i] = poolAvailableSpace{
			Index:      i,

  CHECK(tensorflow::unwrap(concatOp)->OpDef());
  TFE_OpAddInput(concatOp, inputs[0], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  EXPECT_FALSE(tensorflow::unwrap(concatOp)->OpDef())
      << "Inference context is still present";
  TFE_OpAddInput(concatOp, inputs[1], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  tensorflow::AttrValueMap attr_values = ExtractAttrs(concatOp);