%3 = stablehlo.concatenate %2, %1, dim = 0 : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<2x3xf32>
    return %3 : tensor<2x3xf32>
  }

    CharSource concatenated = CharSource.concat(cycle);

    String expected = "abcdabcd";

    // read the first 8 chars manually, since there's no equivalent to ByteSource.slice
    // TODO(cgdecker): Add CharSource.slice?
    StringBuilder builder = new StringBuilder();
    Reader reader = concatenated.openStream(); // no need to worry about closing

    ): FirLiteralExpression? {
        val concatenated = buildString {
            for (arg in stringConcatenationCall.arguments) {
                val evaluated = evaluate(arg) ?: return null
                append(evaluated.value.toString())
            }
        }

        return ConstantValueKind.String.toLiteralExpression(stringConcatenationCall.source, concatenated)
    }

    private fun evaluateFunctionCall(

// allowed.
func.func @parallel_execute_mismatched_output_types() {
  "tf_device.parallel_execute"() ({
// expected-error@-1 {{'tf_device.parallel_execute' op output types must be a concatenated list of output types for each regions.}}
    %0 = "tf.opA"() : () -> (tensor<*xi1>)
    %1 = "tf.opB"() : () -> (tensor<*xi32>)
    tf_device.return %0, %1 : tensor<*xi1>, tensor<*xi32>
  },
  {

                                               TF_ShapeHandle* result,
                                               TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<InferenceContext*>(ctx);
  Status s = cc_ctx->Concatenate(*reinterpret_cast<ShapeHandle*>(first),
                                 *reinterpret_cast<ShapeHandle*>(second),
                                 reinterpret_cast<ShapeHandle*>(result));
  Set_TF_Status_from_Status(status, s);

    ByteSource source = ByteSource.wrap(new byte[] {0, 1, 2, 3});
    Iterable<ByteSource> cycle = Iterables.cycle(ImmutableList.of(source));
    ByteSource concatenated = ByteSource.concat(cycle);

    byte[] expected = {0, 1, 2, 3, 0, 1, 2, 3};
    assertArrayEquals(expected, concatenated.slice(0, 8).read());
  }

  private static final ByteSource BROKEN_CLOSE_SOURCE =
      new TestByteSource(new byte[10], CLOSE_THROWS);

  %15 = mhlo.add %13, %4 : tensor<32x32xi64>
  %16 = mhlo.select %14, %15, %13 : tensor<32x32xi1>, tensor<32x32xi64>
  %17 = mhlo.reshape %16 : (tensor<32x32xi64>) -> tensor<32x32x1xi64>
  %18 = "mhlo.concatenate"(%12, %17) <{dimension = 2 : i64}> : (tensor<32x32x1xi64>, tensor<32x32x1xi64>) -> tensor<32x32x2xi64>

                rhs = IntLiteral [indexes: 29..30, line/column: 3/9..3/10, file: test] (9)
            )""".trimIndent()
        parse(code).assert(expected)
    }

    @Test
    fun `accidentally concatenated lines in a series of assignments`() {
        val code = """
            val a = 1
            val b = 2 val c = 3
            val d = 4
        """.trimIndent()

        val expected = """

// TODO(b/118881356): currently we do not register the QueueEnqueueMany,
// QueueDequeueMany, or QueueDequeueUpTo kernels because they attempt to read
// and write the tensors they access in order to concatenate them into a batch.
// We would need either to call out to an XLA computation to perform the
// concatenation, or we would need to refactor those kernels so the splitting

used to compile C files in that package. All the CPPFLAGS and CXXFLAGS
directives in a package are concatenated and used to compile C++ files in that
package. All the CPPFLAGS and FFLAGS directives in a package are concatenated
and used to compile Fortran files in that package. All the LDFLAGS directives
in any package in the program are concatenated and used at link time. All the