}
	didSearch := size0 != size1
	if didSearch != tt.wantEOFSearch {
		t.Errorf("%d. did EOF search = %v; want %v (size went from %d to %d)", i, didSearch, !didSearch, size0, size1)
	}
	if tt.wantNextReq && numReqs != 2 {
		t.Errorf("%d. numReq = %d; want 2", i, numReqs)
	}
}

// testHandlerBodyConsumer represents a function injected into a test handler to
// vary work done on a request Body.

    auto input_shape = input_type.getShape();

    llvm::SmallVector<int64_t, 4> start;
    llvm::SmallVector<int64_t, 4> size;
    start.reserve(explicit_padding.size() / 2);
    size.reserve(explicit_padding.size() / 2);
    for (int i = 0, e = explicit_padding.size() / 2; i < e; ++i) {
      int64_t pre_padding = explicit_padding[2 * i];
      int64_t post_padding = explicit_padding[2 * i + 1];

  %0 = "tf.Slice"(%arg0, %begins, %sizes) : (tensor<4xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor<1xi32>
  func.return %0 : tensor<1xi32>
}

// -----

func.func @testSlice_wrong_type(%arg0: tensor<28x1x100xf32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<1x1x100xi32> {

  assert(input_shape.size() <= 32);  // Only 32-bit masks are supported.

  // Make sure ranges' ranks are consistent with the input.
  assert(input_shape.size() == begin.size());
  assert(input_shape.size() == end.size());
  assert(input_shape.size() == stride.size());

  for (int i = 0, e = input_shape.size(); i < e; ++i) {
    if (ShapedType::isDynamic(input_shape[i])) continue;

        when:
        succeeds ":util:resolve", ":app:resolve"

        then:
        output.count("files 1: [lib1.jar.size, lib2.jar.size, lib3.jar.size, lib4-1.0.jar.size]") == 2
        output.count("artifacts 1: [lib1.jar.size (project :lib), lib2.jar.size (project :lib), lib3.jar.size (lib3.jar), lib4-1.0.jar.size (org.test.foo:lib4:1.0)]") == 2
        output.count("components 1: [project :lib, project :lib, lib3.jar, org.test.foo:lib4:1.0]") == 2

  int64_t x_row_dim = x_shape[x_shape.size() - 2];
  int64_t x_col_dim = x_shape[x_shape.size() - 1];
  int64_t y_row_dim = y_shape[y_shape.size() - 2];
  int64_t y_col_dim = y_shape[y_shape.size() - 1];
  int64_t out_row_dim = output_shape[output_shape.size() - 2];
  int64_t out_col_dim = output_shape[output_shape.size() - 1];

  int64_t expected_out_row_dim = op.getAdjX() ? x_col_dim : x_row_dim;

    return op.emitOpError() << "requires N to be at least 2, got " << op.getN();

  if (op.getShape().size() != op.getOffset().size())
    return op.emitOpError()
           << "requires sizes of shapes and offsets to be the same, got sizes "
           << op.getShape().size() << " and " << op.getOffset().size();

  auto ranked_dim =
      mlir::dyn_cast<RankedTensorType>(op.getConcatDim().getType());

	size := m.Size()
	dAtA = make([]byte, size)
	n, err := m.MarshalToSizedBuffer(dAtA[:size])
	if err != nil {
		return nil, err
	}
	return dAtA[:n], nil
}

func (m *ContainerResourceMetricSource) MarshalTo(dAtA []byte) (int, error) {
	size := m.Size()
	return m.MarshalToSizedBuffer(dAtA[:size])
}

	size := m.Size()
	dAtA = make([]byte, size)
	n, err := m.MarshalToSizedBuffer(dAtA[:size])
	if err != nil {
		return nil, err
	}
	return dAtA[:n], nil
}

func (m *ContainerResourceMetricSource) MarshalTo(dAtA []byte) (int, error) {
	size := m.Size()
	return m.MarshalToSizedBuffer(dAtA[:size])
}

	size := m.Size()
	dAtA = make([]byte, size)
	n, err := m.MarshalToSizedBuffer(dAtA[:size])
	if err != nil {
		return nil, err
	}
	return dAtA[:n], nil
}

func (m *ContainerResourceMetricSource) MarshalTo(dAtA []byte) (int, error) {
	size := m.Size()
	return m.MarshalToSizedBuffer(dAtA[:size])
}