Mode:       0664,
			Uid:        1000,
			Gid:        1000,
			Uname:      "shane",
			Gname:      "shane",
			Size:       7,
			ModTime:    time.Unix(1350244992, 23960108),
			ChangeTime: time.Unix(1350244992, 23960108),
			AccessTime: time.Unix(1350244992, 23960108),
			Typeflag:   TypeReg,

			testHeader{Header{
				Typeflag: TypeReg,
				Name:     strings.Repeat("longname/", 15) + "file.txt",
				Size:     6,
				Mode:     0644,
				Uid:      501,
				Gid:      20,
				Uname:    "shane",
				Gname:    "staff",
				ModTime:  time.Unix(1360135598, 0),
			}, nil},
			testWrite{"hello\n", 6, nil},
			testClose{nil},
		},
	}, {
		// This file was produced using GNU tar v1.26:

        `max_batch_size` for building TensorRT engines. Add parameters
        `use_dynamic_shape` to enable dynamic shape support. The default is to
        disable dynamic shape support. Add `dynamic_shape_profile_strategy` for
        selecting a dynamic shape profile strategy. The default is profile
        strategy is `Range`.
    *   Issue a warning when function get_tensorrt_rewriter_config is used.

    ShapeHandle shape_handle = c.output(i);
    TF_ShapeAndType& shape = output_shapes_result->items[i];
    shape.num_dims = c.Rank(shape_handle);
    if (shape.num_dims == InferenceContext::kUnknownRank) {
      shape.dims = nullptr;
      continue;
    }
    shape.dims = new int64_t[shape.num_dims];
    for (size_t j = 0; j < shape.num_dims; ++j) {
      shape.dims[j] = c.Value(c.Dim(shape_handle, j));
    }
  }

}

// Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
// result in a zero-sized tensor.
static TF_Tensor* EmptyTensor(TF_DataType dtype,
                              const tensorflow::TensorShape& shape) {
  static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;
  dims.reserve(shape.dims());
  for (int i = 0; i < shape.dims(); ++i) {
    dims.push_back(shape.dim_size(i));

  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());
      if (!status.ok()) out_tensor->reset();
    }
  }
  if (!status.ok()) {

                    <properties>
                        <shade.plugin.version>3.0.0</shade.plugin.version>
                    </properties>
                    <build>
                        <plugins>
                            <plugin>
                                <groupId>org.apache.maven.plugins</groupId>
                                <artifactId>maven-shade-plugin</artifactId>

import okhttp3.internal.connection.ConnectionListener
import okhttp3.internal.connection.RealConnectionPool

/**
 * Manages reuse of HTTP and HTTP/2 connections for reduced network latency. HTTP requests that
 * share the same [Address] may share a [Connection]. This class implements the policy
 * of which connections to keep open for future use.
 *
 * @constructor Create a new connection pool with tuning parameters appropriate for a single-user

	</modules>
	<build>
		<pluginManagement>
			<plugins>
				<plugin>
					<groupId>org.apache.maven.plugins</groupId>
					<artifactId>maven-shade-plugin</artifactId>
					<executions>
						<execution>
							<phase>package</phase>
							<goals>
								<goal>shade</goal>
							</goals>
							<configuration>
								<artifactSet>
									<includes>
										<include>org.dbflute:dbflute-runtime</include>

}

TEST(CAPI, ShapeInferenceError) {
  // TF_FinishOperation should fail if the shape of the added operation cannot
  // be inferred.
  TF_Status* status = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Create this failure by trying to add two nodes with incompatible shapes
  // (A tensor with shape [2] and a tensor with shape [3] cannot be added).
  const char data[] = {1, 2, 3};
  const int64_t vec2_dims[] = {2};