#loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \22tf.Reshape\22(%arg0, %arg1) {_xla_outside_compilation = \220\22} : (tensor<3360x?xi32>, tensor<3xi32>) -> tensor<3x1120x?xi32> loc(#loc9)\0A    %1:3 = ...

      "tf.AssignVariableOp"(%arg0, %read2) : (!tf_res, tensor<32xf32>) -> ()
      %read3 = "tf.ReadVariableOp"(%arg0) : (!tf_res) -> tensor<32xf32>
      tf_executor.yield %read3 : tensor<32xf32>
    }
    tf_executor.fetch %island#0 : tensor<32xf32>
  }
  func.return %graph : tensor<32xf32>
}

// -----
// Tests aliasing of the two resource handles that refer to the same variable.

    %s0 = "tf.Const"() {value = dense<[501, 1, 32, 1280]> : tensor<4xi32>} : () -> tensor<4xi32>
    %s1 = "tf.Const"() {value = dense<[  1, 1,  1, 1280]> : tensor<4xi32>} : () -> tensor<4xi32>

    // CHECK: "tf.BroadcastGradientArgs"
    // CHECK-NOT: _xla_outside_compilation
    %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) {} : (tensor<4xi32>, tensor<4xi32>) -> (tensor<1xi32>, tensor<3xi32>)
    tf_device.return

            %read = "tf.ReadVariableOp"(%cid0) : (!tf_res) -> tensor<32xf32>
            %cst = arith.constant dense<3.0> : tensor<32xf32>
            %cmp = "tf.Less"(%read, %cst) : (tensor<32xf32>, tensor<32xf32>) -> tensor<32xi1>
            %dims = arith.constant dense<0> : tensor<1xi32>
            %reduce = "tf.All"(%cmp, %dims) {keep_dims = false} : (tensor<32xi1>, tensor<1xi32>) -> tensor<i1>
            "tf.Yield"(%reduce) : (tensor<i1>) -> ()

  %2 = "tf.Mul"(%1, %value) {T = "tfdtype$DT_FLOAT"} : (tensor<256x8x7x16xf32>, tensor<16xf32>) -> tensor<256x8x7x16xf32>
  func.return %2 : tensor<256x8x7x16xf32>

// CHECK-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32>
// CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32>

    func.func @main(%arg0: tensor<3x3x10xbf16>, %arg1: tensor<3xi32>) -> tensor<1x?x4xbf16> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0"}} {
      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

func.func @FoldTranspose(%arg0: tensor<1x10x20x3xf32>) -> tensor<1x20x40x16xf32> {
  %cst = arith.constant dense<[1, 20, 40, 16]> : tensor<4xi32>
  %cst_0 = arith.constant dense<[2, 0, 1, 3]> : tensor<4xi32>
  %0 = "tfl.pseudo_qconst"() {qtype = tensor<16x!quant.uniform<i32:f32, 1.8527095877721169E-10>>, value = dense<0> : tensor<16xi32>} : () -> tensor<16x!quant.uniform<i32:f32, 1.8527095877721169E-10>>

// CHECK: Placeholder
// CHECK: key: "type"
// CHECK: type: DT_INT8

func.func @main(%arg0 : tensor<16xf32>) {
  tf_executor.graph {
    %1:2 = tf_executor.island wraps "tf.MlirPassthroughOp"(%arg0) {extra_type_attr = [tensor<5xi32>, tensor<16xf32>], Tinputs = [tensor<16xf32>], Toutputs = [tensor<16xf32>], mlir_module = ""} : (tensor<16xf32>) -> tensor<16xf32>
    tf_executor.fetch
  }
  func.return
}

func.func @plain() {
  tf_executor.graph {

    %assign1_control = tf_executor.island wraps "tf.AssignVariableOp"(%arg1, %read0) : (tensor<*x!tf_type.resource<tensor<32xf32>>>, tensor<32xf32>) -> ()
    %assign2_control = tf_executor.island wraps "tf.AssignVariableOp"(%arg0, %read2) : (tensor<*x!tf_type.resource<tensor<32xf32>>>, tensor<32xf32>) -> ()

  %min = arith.constant dense<0.0> : tensor<16xf32>
  %max = arith.constant dense<15.0> : tensor<16xf32>
  %mini = "tf.Identity"(%min) : (tensor<16xf32>) -> tensor<16xf32>
  %maxi = "tf.Identity"(%max) : (tensor<16xf32>) -> tensor<16xf32>
  %fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<16xf32>, tensor<16xf32>) -> tensor<3x3x3x16xf32>