@Entity
public class Entity1 {

# CHECK-DAG: %[[MUL2:.*]] = "tf.MatMul"(%[[ARG1]], %[[ARG0]])
# CHECK:  %[[IDENTITY:.*]]:2 = "tf.IdentityN"(%[[MUL1]], %[[MUL2]])
# CHECK: return %[[IDENTITY]]#1, %[[IDENTITY]]#0


def Test():

  x = tf.constant(1.0, shape=(5, 3))
  y = tf.constant(1.0, shape=(3, 5))

  s = tf.matmul(x, y)
  t = tf.matmul(y, x)
  [t, s] = array_ops.identity_n([t, s])

  tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)

import gradlebuild.basics.releasedVersionsFile
import gradlebuild.basics.repoRoot
import gradlebuild.identity.extension.ModuleIdentityExtension
import gradlebuild.identity.extension.ReleasedVersionsDetails
import gradlebuild.identity.provider.BuildTimestampFromBuildReceiptValueSource
import gradlebuild.identity.provider.BuildTimestampValueSource
import gradlebuild.identity.tasks.BuildReceipt

plugins {
    `java-base`
}

// CHECK: %[[IDENTITY_1:.*]] = "tf.Identity"(%[[W_1]]) : (tensor<2x3x3x1024xi8>) -> tensor<2x3x3x1024xi8>
// CHECK: %[[DEQUANTIZED_1:.*]] = "tf.PartitionedCall"(%[[IDENTITY_1]]) <{config = "", config_proto = "", executor_type = "", f = @composite_dequantize_uniform__}> : (tensor<2x3x3x1024xi8>) -> tensor<2x3x3x1024xf32>
// CHECK: %[[IDENTITY_2:.*]] = "tf.Identity"(%[[W_2]]) : (tensor<3x3x1024x1xi8>) -> tensor<3x3x1024x1xi8>

# PRESERVE:           %[[IDENTITY_N:.*]]:3, %{{.*}} = tf_executor.island(%[[ASSIGN_ADD_CTRL]]) wraps "tf.IdentityN"
# PRESERVE:           %[[ADD:.*]], %{{.*}} = tf_executor.island wraps "tf.Add"(%[[ARG_0]], %[[ARG_1]])

  } else if (yield.getNumOperands() == 1) {
    Value operand = yield.getOperand(0);
    auto identity = builder.create<TF::IdentityOp>(island.getLoc(),
                                                   operand.getType(), operand);
    yield.setOperand(0, identity.getOutput());
  } else {
    auto identity_n = builder.create<TF::IdentityNOp>(
        island.getLoc(), yield.getOperandTypes(), yield.getOperands());

namespace mlir {
namespace TFTPU {

namespace {

// This pass removes Identity/IdentityN ops from the TPU computation and
// reachable functions.
// TODO(lyandy): Remove this pass once resource op lifting is migrated to use
// resource alias analysis and support region based control flow. Removing
// Identity ops may remove `_XlaSharding` annotation attribute if Identity ops
// are used to propagate such information.

//
//   ```python
//   with tf.get_default_graph().gradient_override_map(
//       {'IdentityN': 'OverrideGradientWithG'}):
//     y, _ = identity_n([f(x), x])
//
//   @tf.RegisterGradient('OverrideGradientWithG')
//   def ApplyG(op, dy, _):
//     return [None, g(dy)]  # Do not backprop to f(x).
//   ```
Status IdentityN(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> input,

	}

	// Initialize
	li, err := cr.NewCustomTokenCredentials(stsEndpoint, token, roleArn, opts...)
	if err != nil {
		log.Fatalf("Error initializing CustomToken Identity: %v", err)
	}

	v, err := li.Get()
	if err != nil {
		log.Fatalf("Error retrieving STS credentials: %v", err)
	}

	if displayCreds {
		fmt.Println("Only displaying credentials:")

    %0:3 = tf_executor.island {
      %1:2 = "tf.IdentityN"(%arg0, %arg1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")
      %2:2 = "tf.IdentityN"(%1#0, %1#1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")
      tf_executor.yield %2#0, %2#1 : tensor<i32>, tensor<f32> loc("identity@some_function")
    } loc("identity@some_function")
    tf_executor.fetch
  }