// CHECK: "tf.Conv3DBackpropInputV2"
}

func.func @mul_i64(%arg0: tensor<14xi64>, %arg1: tensor<14xi64>) -> tensor<14xi64> {
  %0 = "tf.Mul"(%arg0, %arg1) : (tensor<14xi64>, tensor<14xi64>) -> tensor<14xi64>
  func.return %0: tensor<14xi64>

// CHECK-LABEL: mul_i64
// CHECK:  tfl.mul %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<14xi64>
// CHECK:  return
}

    func.return %2, %3 : tensor<1x10xf32>, tensor<1x10xi64>
  }

  // CHECK: %[[RESOURCE:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "a"}> : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
  // CHECK: %[[RESOURCE_1:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "b"}> : () -> tensor<!tf_type.resource<tensor<1x10xi64>>>

    %16 = "tf.BroadcastArgs"(%11, %14) : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
    %17 = "tf.Concat"(%cst_2, %16, %12) : (tensor<i32>, tensor<1xi64>, tensor<2xi64>) -> tensor<3xi64>
    %18 = "tf.Concat"(%cst_2, %16, %15) : (tensor<i32>, tensor<1xi64>, tensor<2xi64>) -> tensor<3xi64>
    %19 = "tf.BroadcastTo"(%4, %17) : (tensor<2x?x?xi8>, tensor<3xi64>) -> tensor<2x?x?xi8>

  func.return %cst : tensor<1x750xf32>

  // CHECK-DAG: %0 = mhlo.constant dense<7.680000e+02> : tensor<f32>
  // CHECK: %1 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<f32>) -> tensor<1x750xf32>
  // CHECK: return %1 : tensor<1x750xf32>
}

// CHECK-LABEL: @unfold_splat_constant_integer
func.func @unfold_splat_constant_integer() -> tensor<1x750xi32> {

  // CHECK: "mhlo.all_reduce"
  // CHECK-SAME: channel_handle = #mhlo.channel_handle<handle = 10001, type = 1>
  // CHECK-SAME{LITERAL}: replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
  // CHECK: mhlo.add
  // CHECK: mhlo.return
  %0 = "tf.CollectiveReduceV2"(%input, %group_size, %group_key, %instance_key) {merge_op = "Add", final_op = "Id"} : (tensor<f32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<f32>

  // CHECK-DAG: %[[SCALE_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[SCALE]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<256xf32>) -> tensor<4x256xf32>
  // CHECK-DAG: %[[OFFSET_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[OFFSET]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<256xf32>) -> tensor<4x256xf32>

// CHECK: %[[XLA_CALL_MODULE:.+]] = "tf.XlaCallModule"(%[[CUSTOM_AGGREGATOR_0]], %[[CST]])
// CHECK-SAME: _entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1"

      %0 = mhlo.constant dense<1.000000e+00> : tensor<f64>
      %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64>
      %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64>
      %3 = "mhlo.dynamic_iota"(%2) {iota_dimension = 0 : i64} : (tensor<1xi64>) -> tensor<?xi32>
      %4 = mhlo.multiply %3, %3 : tensor<?xi32>
      return %4 : tensor<?xi32>
    }
  })";
  CellReader<int64_t> legal_error(kFailedLegalizationStreamz);

  %2 = "mhlo.dynamic_broadcast_in_dim"(%cst_0, %1) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>, tensor<4xindex>) -> tensor<?x256x256x2xf32>
  %3 = mhlo.multiply %0, %2 : tensor<?x256x256x2xf32>
  %4 = "mhlo.dynamic_broadcast_in_dim"(%cst_1, %1) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>, tensor<4xindex>) -> tensor<?x256x256x2xf32>
  %5 = mhlo.add %3, %4 : tensor<?x256x256x2xf32>

  %begins = "tf.Const"() {value = dense<[-1]> : tensor<1xi64>} : () -> (tensor<1xi64>)
  %sizes = "tf.Const"() {value = dense<[2]> : tensor<1xi64>} : () -> (tensor<1xi64>)
  // expected-error @+1 {{requires 0 <= begin[i] <= begin[i] + size[i] <= Di}}
  %0 = "tf.Slice"(%arg0, %begins, %sizes) : (tensor<4xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}

// -----