// 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]+}}(

    func.return %1 : tensor<100x50xf32>
  }

  // CHECK-LABEL: func @SessionInitializerFunction()
  // CHECK-SAME: tf_saved_model.exported_names = ["SessionInitializerFunction"]
  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"
  // CHECK-SAME: "var1"
  // CHECK: %[[CST:.*]] = arith.constant dense<> : tensor<0xf32>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[CST]])
}

func.func @transposeFusedBatchNormV3_f32(
  %arg0: tensor<1x28x28x64xf32>,
  %arg1: tensor<64xf32>
) -> tensor<1x28x28x64xf32> {

  // CHECK: "tf.FusedBatchNormV3"
  // CHECK-SAME: (%[[X_TRANSPOSE:[0-9]*]], %arg1, %arg1, %arg1, %arg1)
  // CHECK-SAME: data_format = "NCHW"
  %y, %batch_mean, %batch_var, %reserve_1, %reserve_2, %reserve_3
    = "tf.FusedBatchNormV3"(%arg0, %arg1, %arg1, %arg1, %arg1)
       {
         data_format = "NHWC",

// CHECK: %[[q2:.*]] = "quantfork.qcast"(%[[reshape]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[call:.*]] = "tf.PartitionedCall"(%[[dq2]]
// CHECK-SAME: f = @composite_matmul_with_bias_fn_1
// CHECK: %[[q3:.*]] = "quantfork.qcast"(%[[call]])
// CHECK-SAME: quant.uniform<i8:f32, 0.015686274509803921:-1>

// 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: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[reshape:.*]] = "tf.Reshape"(%[[dq1]]
// CHECK: %[[q2:.*]] = "quantfork.qcast"(%[[reshape]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])

  // 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