}

func.func @matmulNoTransposeAOrB(%arg0: tensor<1x1280xf32>, %arg1: tensor<1280x1000xf32>) -> tensor<1x1000xf32> {
  %166 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", _output_shapes = ["tfshape$dim { size = 1} dim { size = 1000}"], device = "", name = "matmul", transpose_a = false, transpose_b = false} : (tensor<1x1280xf32>, tensor<1280x1000xf32>) -> tensor<1x1000xf32>
  func.return %166 : tensor<1x1000xf32>

    func.return %1 : tensor<2xf32>
  }

  // CHECK-LABEL: binary_op_broadcast
  func.func @binary_op_broadcast(%arg0: tensor<4x1xf32>, %arg1: tensor<4x1x4xf32>) -> tensor<4x4x4xf32> {
    // CHECK: %[[BROADCAST0:.*]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>}> : (tensor<4x1xf32>) -> tensor<4x4x1xf32>
    // CHECK: %[[RESHAPE0:.*]] = mhlo.reshape %[[BROADCAST0]] : (tensor<4x4x1xf32>) -> tensor<4x4xf32>

  %0 = mhlo.reshape %arg1 : (tensor<1x72x128xf32>) -> tensor<72x128xf32>
  %1 = "mhlo.dot_general"(%arg0, %0) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [0]
    >} : (tensor<1x72x72xf32>, tensor<72x128xf32>) -> tensor<1x72x128xf32>
  func.return %1 : tensor<1x72x128xf32>

// CHECK-SAME: (tensor<1x37xf32>, tensor<40x37xf32>, none) -> tensor<1x40xf32>
  func.return %1 : tensor<1x40xf32>
}

// CHECK-LABEL: RemoveLstmQuantZeroBias
func.func @RemoveLstmQuantZeroBias(
  %arg0: tensor<1x528xf32>,
  %arg1: tensor<2048x528xf32>,
  %arg2: tensor<2048x528xf32>,
  %arg3: tensor<2048x528xf32>,
  %arg4: tensor<2048x528xf32>,
  %arg5: tensor<2048x640xf32>,
  %arg6: tensor<2048x640xf32>,

    // CHECK: (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<128xf32>
    %3 = "tf.Restore"(%arg0, %cst_0) {device = ""} : (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<*xf32>
    "tf.AssignVariableOp"(%2, %3) : (tensor<!tf_type.resource<tensor<128xf32>>>, tensor<*xf32>) -> ()

//                (tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>)
//
// will be converted into:
//
//   %0 = "mhlo.slice"(%input) {
//             limit_indices = dense<[4, 2]> : tensor<2xi64>,
//             start_indices = dense<0> : tensor<2xi64>,
//             strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>
//   %1 = "mhlo.slice"(%input) {

}

// -----

// 'input_to_output_weights' input for lstm op has unmatched rank with `input`.