// CHECK-SAME: tf_quant.quantized_ops = ["DepthwiseConv2D", "BiasAdd", "Relu"]
// CHECK: func private @quantized_matmul_with_bias_fn
// CHECK: func private @quantized_matmul_with_bias_and_relu_fn
// CHECK: func private @quantized_matmul_with_bias_and_relu6_fn
// CHECK: func private @quantized_matmul_fn
// CHECK-SAME: tf_quant.quantized_ops = ["MatMul"]

# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer

# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR:[a-zA-Z_0-9]+]]", type = tensor<1x3xf32>, value = {{.*}} : tensor<1x3xf32>}> : () -> ()
# CHECK-NOT: session_initializer

# CHECK:      func {{@[a-zA-Z_0-9]+}}(

// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]], %[[w_quant]], %[[b_quant]],
// CHECK-SAME: %[[in_scale]], %[[in_zp]], %[[w_scale]], %[[w_zp]],
// CHECK-SAME: %[[b_scale]], %[[w_zp]], %[[out_scale]], %[[out_zp]])
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_fn_0

  // CHECK-SAME: %arg21: tensor<!tf_type.resource<tensor<640xf32>>>
  // CHECK-SAME: %arg22: tensor<!tf_type.resource<tensor<640xf32>>>
  // CHECK-SAME: %arg23: tensor<!tf_type.resource<tensor<512x640xf32>>>
  // CHECK-SAME: %arg24: tensor<!tf_type.resource<tensor<640x640xf32>>>
  // CHECK-SAME: %arg25: tensor<!tf_type.resource<tensor<4096xf32>>>
  // CHECK-SAME: %arg26: tensor<!tf_type.resource<tensor<640x4096xf32>>>

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-SAME: is_mutable
  // CHECK-SAME: }> : () -> ()
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.> : tensor<f32> } : () -> ()

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-SAME: is_mutable
  // CHECK-SAME: }> : () -> ()

  // CHECK-SAME: _XlaSharding = ""
  // CHECK-SAME: partition_dims = []
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG0]], [[OUTPUT]]#0)
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG1]], [[OUTPUT]]#1)
  "tf.AssignVariableOp"(%0, %2) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @read_write_packed_resource

// RUN: tf-opt -split-input-file -verify-diagnostics -xla-legalize-tf-collective -xla-legalize-tf %s | FileCheck %s


// CHECK: module attributes
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_key = 0
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_size = 2
// CHECK-LABEL: func @all_reduce_cross_replica
func.func @all_reduce_cross_replica(%input: tensor<f32>) -> tensor<f32> {

  }

// CHECK-LABEL: func @conv_with_single_layer

// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%arg0
// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]]
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

// tf_device.Cluster.

// CHECK-LABEL: func @testReadVariableOpColocated
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
func.func @testReadVariableOpColocated(%arg0: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
  // CHECK:      tf_device.replicate
  // CHECK-SAME: (%[[ARG0]] as %[[RI_0:[a-z0-9]*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)

  func.return
}

// CHECK:      tf_device.replicate
// CHECK-SAME: [%[[ARG_0]], %[[ARG_0]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_2]], %[[ARG_2]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_4]], %[[ARG_4]]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_1]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_3]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_5]] as %{{[a-z0-9]*}}