model_metadata_buffer->data =
      std::vector<uint8_t>(expected_value.begin(), expected_value.end());
  input_model.buffers.push_back(std::move(model_metadata_buffer));
  auto metadata_t = std::make_unique<tflite::MetadataT>();
  metadata_t->name = tflite::optimize::kTfLiteReducedPrecisionKey;
  metadata_t->buffer = input_model.buffers.size() - 1;
  input_model.metadata.push_back(std::move(metadata_t));

  // Sparsify and create output model

    @Test
    fun `defers evaluation of Callable objects`() {
        assertDeferredEvaluationOf(callable()) {
            call()
        }
    }

    @Test
    fun `defers evaluation of dynamic Callable fields`() {
        assertDeferredEvaluationOf(BeanOf(callable())) {
            value.call()
        }
    }

    @Test
    fun `defers evaluation of static Callable fields`() {

    @Test
    fun `defers evaluation of Function objects`() {
        assertDeferredEvaluationOf(function()) {
            invoke()
        }
    }

    @Test
    fun `defers evaluation of dynamic Function fields`() {
        assertDeferredEvaluationOf(BeanOf(function())) {
            value()
        }
    }

    @Test
    fun `defers evaluation of static Function fields`() {

            """
        )

        and: "multiple sub-projects"
        settingsFile << """
            include 'foo:foo'
            include 'bar:bar'
        """

        // Make the classpath of :foo differ from :bar's
        // thus causing :foo:foo and :bar:bar to have separate ClassLoaders.
        File someLib = file('lib/someLib.jar')
        jarWithClasses(someLib, SomeClass: 'class SomeClass {}')

          const int buffer_idx = tensor->buffer;
          const int expected_buffer_idx = expected_tensor->buffer;
          const auto buffer = model.buffers[buffer_idx].get()->data;
          const auto expected_buffer =
              expected_model.buffers[expected_buffer_idx].get()->data;
          EXPECT_THAT(buffer, Eq(expected_buffer));
        }
      }
    }
  }
}

// form NAME=VALUE and changes the default settings
// of the named environment variables to the given values.
//
// The -changed flag prints only those settings whose effective
// value differs from the default value that would be obtained in
// an empty environment with no prior uses of the -w flag.
//
// For more about environment variables, see 'go help environment'.
//
// # Update packages to use new APIs

        // Otherwise, do additional checks for data type and buffer contents.
        const std::vector<uint8_t> quantized_buffer =
            quant_model->buffers[quantized_tensor->buffer()].get()->data;
        const std::vector<uint8_t> float_buffer =
            float_model->buffers[float_tensor->buffer()].get()->data;
        if ((quantized_buffer == float_buffer) &&
            (quantized_tensor->type() == float_tensor->type())) {

github.com/gogo/protobuf
https://github.com/gogo/protobuf
----------------------------------------------------------------
Copyright (c) 2013, The GoGo Authors. All rights reserved.

Protocol Buffers for Go with Gadgets

Go support for Protocol Buffers - Google's data interchange format

Copyright 2010 The Go Authors.  All rights reserved.
https://github.com/golang/protobuf

  for (const auto& metadata : input_model.metadata) {
    if (metadata->name != tflite::optimize::kTfLiteReducedPrecisionKey) {
      continue;
    }

    const auto& data = input_model.buffers[metadata->buffer]->data;
    options.metadata[metadata->name] = std::string(data.begin(), data.end());
    break;
  }

  if (!tflite::MlirToFlatBufferTranslateFunction(module.get(), options,

          kernel_input_feature_dim, kernel_output_feature_dim,
          kernel_spatial_dimensions, batch_dim, feature_dim, spatial_dims));
}

// Converts a TF::BiasAddOp to HLO.
// This differs from a normal TF::AddOp with respect to how the data_format
// is handled, which can optionally require a general broadcast of the
// 'bias' term in a way that is not compatible with the standard left-padded