// then:
                    require("Plugin Using Embedded Kotlin " in result.output)
                }
            }

            """
        )

        assertThat(
            outputOf("test", "-i"),
            containsString("Plugin Using Embedded Kotlin ")
        )
    }

    @Test

Type 'Writer' property 'output' is not writable because '${location}' is not a directory.

Reason: Expected '${location}' to be a directory but it's a file.

Possible solution: Make sure that the 'output' is configured to a directory.

${validationMessage("cannot_write_output")}
"""

        when:
        render cannotWriteToDir {
            type('Writer').property('output')
            dir(location)

    }
    tf_executor.fetch %outputs#0, %outputs#1 : tensor<i1>, tensor<i32>
  }
  return
}

// CHECK: [[OUTPUT_0:%.*]], {{%.*}} = tf_executor.island wraps "tf.opA"() {_parallel_execution_ids = "p0:0"}
// CHECK: [[OUTPUT_1:%.*]], {{%.*}} = tf_executor.island wraps "tf.opB"([[OUTPUT_0:%.*]]) {_parallel_execution_ids = "p0:0"}

	input8, output8 = output8, input8
	input16, output16 = output16, input16
	input32, output32 = output32, input32
	input64, output64 = output64, input64
	in := 0
	out := 13
	for i := range input8 {
		input8[i] = u8(in)
		output8[i] = u8(out)
		outputS[i] = u8(out)
		input16[i] = u16(in)
		output16[i] = u16(out)
		input32[i] = u32(in)
		output32[i] = u32(out)
		input64[i] = u64(in)

    %cst = stablehlo.constant dense<0.000000e+00>: tensor<10x1024x3xf32>

        output.contains 'Output: nested.outputFiles$2 [output-nested-2.txt]'
        output.contains 'Output: outputDirectories$1 [outputs1]'
        output.contains 'Output: outputDirectories$2 [outputs2]'
        output.contains "Output: outputDirectory [outputs]"
        output.contains "Output: outputFile [output.txt]"
        output.contains 'Output: outputFiles$1 [output1.txt]'

                            const std::vector<Output>& outputs,
                            const std::vector<Output>& inputs,
                            std::vector<Output>* grad_outputs) {
  std::vector<Output> grad_inputs;
  grad_inputs.reserve(outputs.size());
  for (const Output& output : outputs) {
    grad_inputs.emplace_back(ops::OnesLike(scope, output));
  }

  %0, %1 = tf_executor.graph {
    %output_1, %token_1, %control_1 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    %output_2, %token_2, %control_2 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    tf_executor.NextIteration.Sink [%token_1] %output_1 : tensor<1xi32> {T = i32, device = ""}
    tf_executor.NextIteration.Sink [%token_2] %output_2 : tensor<1xi32> {T = i32, device = ""}

  %graph_result = tf_executor.graph {
    %output0, %control0 = tf_executor.island {
      %a = "tf.opA"(%arg0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %a : tensor<i32>
    }
    %output1, %control1 = tf_executor.island {
      %b = "tf.opB"(%output0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %b : tensor<i32>
    }
    tf_executor.fetch %output1 : tensor<i32>
  }

      }
      input_arg {
        name: "input"
        type: DT_INT32
      }
      output_arg {
        name: "output"
        type: DT_INT32
      }
      output_arg {
        name: "output_1"
        type: DT_INT32
      }
      output_arg {
        name: "output_2"
        type: DT_INT32
      }
    }
    node_def {
      name: "Add/y"
      op: "Const"
      attr {