// CHECK-DAG: %[[input_padded:.*]] = "tf.PadV2"(%{{.*}}, %[[padding_rank_2]], {{.*}}) : (tensor<?x?x?x3xi8>, tensor<4x2xi32>, tensor<i8>) -> tensor<?x?x?x3xi8>

         padding = "EXPLICIT",
         strides = [5, 6, 7, 8]
       } : (tensor<1x32x32x3xf32>, tensor<4xi32>, tensor<1x32x32x8xf32>)
         -> tensor<1x1x3x8xf32>

  func.return %0 : tensor<1x1x3x8xf32>
}

// CHECK-LABEL: func @transposeConv2DBackpropInput
func.func @transposeConv2DBackpropInput(
  %input_sizes: tensor<4xi32>,
  %filter: tensor<1x1x3x8xf32>,
  %out_backprop: tensor<1x32x32x8xf32>

  // CHECK: %[[RES1:.*]] = "tfl.batch_matmul"(%[[RES0]], %arg2) <{adj_x = false, adj_y = false, asymmetric_quantize_inputs = false}> : (tensor<8x4xf32>, tensor<4x256xf32>) -> tensor<8x256xf32>
  %2 = "tfl.batch_matmul"(%1, %arg2) {adj_x = true, adj_y = false, asymmetric_quantize_inputs = false} : (tensor<4x8xf32>, tensor<4x256xf32>) -> tensor<8x256xf32>
  func.return %2 : tensor<8x256xf32>

  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<8xi32>) -> tensor<8xi32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "input"
# CHECK:         %[[GRAPH:[0-9]+]] = tf_executor.graph
# CHECK:           tf_executor.fetch %[[ARG_0]]

}

// CHECK-LABEL: conv2d_backprop_input_with_add
func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> {
  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<8xi32> loc({{.*}})) -> tensor<8xi32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "arg"
# CHECK-SAME:  outputs = "arg"
# CHECK:         %[[GRAPH:[0-9]+]] = tf_executor.graph
# CHECK:           tf_executor.fetch %[[ARG_0]]

  // CHECK-LABEL @lowers_string_ops
  // CHECK-DAG: ml_program.global public @vars.Variable_1([]) : tensor<!tf_type.string>
  func.func @lowers_string_ops(%arg0: tensor<128xi32>, %arg1: tensor<128xi32>, %arg2: tensor<128x1xi32>, %arg3: tensor<128x90xi32>, %arg4: tensor<128x90xi32>, %arg5: tensor<128x90xi32>, %arg6: tensor<128x90x64xf32>, %arg7: tensor<128x90x64xf32>) -> tensor<!tf_type.string> {

         padding = "VALID",
         strides = [1, 1, 1, 1]
       } : (tensor<1x28x28x64xf16>, tensor<4xi32>, tensor<1x28x28x64xf16>)
        -> tensor<1x1x64x64xf16>

  func.return %0 : tensor<1x1x64x64xf16>
}

// CHECK-LABEL: func @transposeConv2DBackpropInput_f16
func.func @transposeConv2DBackpropInput_f16(
  %input_size:   tensor<4xi32>,
  %filter:       tensor<1x28x28x64xf16>,
  %out_backprop: tensor<1x28x28x64xf16>

      %outputs_23, %control_24 = tf_executor.island(%control_18) wraps "tf.Const"() {_tpu_replicate = "cluster_sample_sequence", device = "", value = dense<[3, 32, 32, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
      %outputs_25, %control_26 = tf_executor.island(%control_18) wraps "tf.TPUCompilationResult"() {_tpu_compilation_status = "cluster_sample_sequence", device = ""} : () -> tensor<!tf_type.string>

    %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<*x!tf_type.resource<tensor<*xi32>>>
    %cst = arith.constant dense<2> : tensor<4xi32>
    "tf.AssignAddVariableOp"(%0, %cst) {} : (tensor<*x!tf_type.resource<tensor<*xi32>>>, tensor<4xi32>) -> ()
    %1 = "tf.ReadVariableOp"(%0) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>) -> tensor<*xi32>
    func.return %1 : tensor<*xi32>
  }
}