`<FieldC>A.C.C</FieldC>` +
			`<EmbedB>` +
			`<FieldB>A.B.B</FieldB>` +
			`<FieldA>` +
			`<A1>A.B.C.A1</A1>` +
			`<A2>A.B.C.A2</A2>` +
			`</FieldA>` +
			`<FieldC>A.B.C.C</FieldC>` +
			`</EmbedB>` +
			`<FieldA>A.A</FieldA>` +
			`<FieldE>A.D.E</FieldE>` +
			`</EmbedA>`,
	},

	// Anonymous struct pointer field which is nil
	{
		Value:     &EmbedB{},

			},
		},
		{name: "preserve unknown fields",
			obj: map[string]interface{}{
				"withUnknown": map[string]interface{}{
					"field1": "a",
					"field2": "b",
				},
				"withUnknownList": []interface{}{
					map[string]interface{}{
						"field1": "a",
						"field2": "b",
					},
					map[string]interface{}{
						"field1": "x",
						"field2": "y",
					},
					map[string]interface{}{

        rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

    dataset_path = self.create_tempfile('tfrecord').full_path

		}
		_ = stmt.Schema.ParseIndexes()
		field := stmt.Schema.LookUpField(fieldName)
		if field == nil {
			t.Fatalf("%v: failed to find column %q", utils.FileWithLineNum(), fieldName)
		}
		if field.Unique != unique {
			t.Fatalf("%v: %q column %q unique should be %v but got %v", utils.FileWithLineNum(), stmt.Schema.Table, fieldName, unique, field.Unique)
		}
		if field.UniqueIndex != uniqueIndex {

  %graph = tf_executor.graph {
    %island, %ctrl = tf_executor.island {
      %pred = "tf.SomeOp"(%arg2) : (tensor<f32>) -> tensor<i32>
      tf_executor.yield %pred : tensor<i32>
    }
    tf_executor.fetch %island : tensor<i32>
  }
  func.return %graph : tensor<i32>
}

// CHECK-LABEL:   func @simple_independent_chains

module {
func.func @simpleWhile(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tfl.while"(%arg0) ({
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  },{
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  }) {tac.device = "CPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}

  regions accept the current value of the iteration variables as inputs.

  The condition region yields a tensor<i1> which, if false, will exit the loop.
  It can also, optionally and additionally, yield the iteration variables, which
  must be unchanged.

  The body region always has to yield the (possibly updated) iteration variables.

  The iteration variables are initialized to the Op input, and the results of the

// isPaddedField reports whether the i'th field of struct type t is followed
// by padding.
func isPaddedField(t Type, i int) bool {
	field := t.Field(i)
	if i+1 < t.NumField() {
		return field.Offset+field.Type.Size() != t.Field(i+1).Offset
	}
	return field.Offset+field.Type.Size() != t.Size()
}

// StructOf returns the struct type containing fields.
// The Offset and Index fields are ignored and computed as they would be

// are all in the testing namespace:
//
//
//  Range(begin, end [, step]) - Yields values {begin, begin+step,
//                               begin+step+step, ...}. The values do not
//                               include end. step defaults to 1.
//  Values(v1, v2, ..., vN)    - Yields values {v1, v2, ..., vN}.
//  ValuesIn(container)        - Yields values from a C-style array, an STL

        and dtypes.int64 for int
      num_examples: Number of examples in the representative dataset.

    Yields:
      data_gen: A `quantize_model._RepresentativeSample` filled with random
        values.
    """
    for _ in range(num_examples):
      yield {input_key: random_ops.random_uniform(shape, minval, maxval, dtype)}

  def _save_tf1_model(
      self,
      sess: session.Session,