%cst_0 = "tf.Const"() {device = "", value = dense<[3, 4, 5, 6]> : tensor<4xi32>} : () -> tensor<4xi32>
        %1 = "tf.VarHandleOp"() {container = "", device = "/device:CPU:0", shared_name = "var_1"} : () -> tensor<!tf_type.resource<tensor<4xi32>>>
        "tf.AssignVariableOp"(%1, %cst_0) : (tensor<!tf_type.resource<tensor<4xi32>>>, tensor<4xi32>) -> ()

        return
      }
    }
  )mlir";

  %cst = arith.constant dense<[-1, 7]> : tensor<2xi32>
  %0 = "tf.TensorListReserve"(%cst, %arg0) : (tensor<2xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<?x7xf32>>>
  %1 = "tf.TensorListGetItem"(%0, %arg1, %cst) : (tensor<!tf_type.variant<tensor<?x7xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<?x7xf32>
  func.return %1 : tensor<?x7xf32>

func.func @const() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

    func.func @tpu_partitioned_call_func(%arg0: tensor<?xi32>) -> (tensor<?xi32>) {
      func.return %arg0 : tensor<?xi32>
    }

    func.func @main(%arg0: tensor<20xi32>, %arg1: tensor<?xi32>) -> tensor<*xi32> {
      %2 = "tf.TPUPartitionedCall"(%arg0, %arg1) {f = @tpu_partitioned_call_func} : (tensor<20xi32>, tensor<?xi32>) -> tensor<*xi32>
      func.return %2 : tensor<*xi32>
    }
  })";

  %graph:2 = tf_executor.graph {
    %island:3 = tf_executor.island {
      %add1 = "tf.Add"(%arg0, %arg1) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
      %add2 = "tf.Add"(%add1, %arg1) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
      tf_executor.yield %add1, %add2 : tensor<*xi32>, tensor<*xi32>
    }
    tf_executor.fetch %island#0, %island#1 : tensor<*xi32>, tensor<*xi32>
  }

    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<4xi32>) -> tensor<4xi32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "output"
# CHECK-NEXT:    return %[[ARG_0]] : tensor<4xi32>

         strides = [1, 1, 1, 1]
       } : (tensor<1x28x28x64xf32>, tensor<4xi32>, tensor<1x28x28x64xf32>)
        -> tensor<1x1x64x64xf32>

  func.return %0 : tensor<1x1x64x64xf32>
}

// CHECK-LABEL: func @transposeConv2DBackpropFilter_f16
func.func @transposeConv2DBackpropFilter_f16(
  %input:        tensor<1x64x28x28xf16>,
  %filter_size:  tensor<4xi32>,
  %out_backprop: tensor<1x64x28x28xf16>
) -> tensor<1x1x64x64xf16> {

// CHECK-NEXT:    buffer: 2
// CHECK-NEXT:  } ],
// CHECK-NEXT:  signature_defs: [  ]
// CHECK-NEXT: }
func.func @main(%arg0 : tensor<!tf_type.variant<tensor<2xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> {
  func.return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>

// CHECK-NEXT: tfl.gather
// CHECK-NEXT: tfl.dequantize
}

// CHECK-LABEL: dequantize_pushdown_gather_with_reduction
func.func @dequantize_pushdown_gather_with_reduction(%arg0: tensor<2xi32>) -> tensor<2x2xf32> {
  %w = "tfl.pseudo_qconst"() {qtype = tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<12x2xi8>} : () -> tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>

    func.func @main(%arg0: tensor<?xi32, #mhlo.type_extensions<bounds = [1]>>) -> tensor<?xi32, #mhlo.type_extensions<bounds = [1]>> {
      %0 = "tf.Identity"(%arg0) : (tensor<?xi32, #mhlo.type_extensions<bounds = [1]>>) -> tensor<?xi32, #mhlo.type_extensions<bounds = [1]>>
      func.return %0 : tensor<?xi32, #mhlo.type_extensions<bounds = [1]>>
    }
  })";