return llvm::to_vector(llvm::map_range(shapes, [](int64_t shape) {
    return shape == kTFDynamicSize ? mlir::ShapedType::kDynamic : shape;
  }));
}

llvm::SmallVector<int64_t> ConvertMlirShapeToTF(
    llvm::ArrayRef<int64_t> shapes) {
  return llvm::to_vector(llvm::map_range(shapes, [](int64_t shape) {
    return mlir::ShapedType::isDynamic(shape) ? kTFDynamicSize : shape;
  }));
}

  Output y = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(y_shape));
  auto z = Einsum(scope_, {x, y}, "i,i->");
  TensorShape z_shape({});
  RunTest({x, y}, {x_shape, y_shape}, {z}, {z_shape});
}

TEST_F(LinalgGradTest, Einsum_OuterProduct) {
  TensorShape x_shape({3});
  TensorShape y_shape({5});
  Output x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));

// Test case: Basic converting.

func.func @f() {
  // CHECK: "tf.VarHandleOp"
  // CHECK: "tf.ReadVariableOp"
  %val0 = "tf.VariableV2"() {_class = ["loc:@v"], container = "", device = "", shape = #tf_type.shape<96>, shared_name = ""} : () -> tensor<96x!tf_type.f32ref>
  %val1 = "tf.Identity"(%val0) : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>
  func.return
}

// -----

     */
    int connectionState;
    int tid;

    String share;
    String service = "?????";
    String service0;
    SmbSession session;
    boolean inDfs, inDomainDfs;
    int tree_num; // used by SmbFile.isOpen

    SmbTree( SmbSession session, String share, String service ) {
        this.session = session;
        this.share = share.toUpperCase();
        if( service != null && service.startsWith( "??" ) == false ) {

   node [fontname = "Sans"];
   edge [fontname = "Sans"];

   "apiElements" [shape="box", label=<<B>apiElements</B>(C)>, color="pink", bgcolor="pink", style="filled"]
   "apiElements" -> "api"
   "apiElements" -> "compileOnlyApi"

   "api" [shape="box", label=<<B>api</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]
   "compileOnlyApi" [shape="box", label=<<B>compileOnlyApi</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]

  auto c_api_client = down_cast<xla::PjRtCApiClient*>(pjrt_client->get());
  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});

  auto buffer = c_api_client->pjrt_c_client()->client->BufferFromHostBuffer(
      data.data(), shape.element_type(), shape.dimensions(),
      /*byte_strides=*/std::nullopt,
      xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,

  %0 = "tf.AnonymousIteratorV3"() {output_shapes = [
    #tf_type.shape<200x28x28x1>,
    #tf_type.shape<200x10>], output_types = [f32, f32]} : () -> tensor<!tf_type.resource>
  // CHECK: "tf.Iterator"
  // CHECK-SAME: shared_name = "_iterator2"
  %1 = "tf.AnonymousIteratorV3"() {output_shapes = [
    #tf_type.shape<200x10>], output_types = [f32]} : () -> tensor<!tf_type.resource>
  return
}

// -----

  return %arg4 : tensor<10xf32>
}

// -----

// CHECK-LABEL: strips_attributes_with_tf_values
// CHECK-NOT: tf
func.func @strips_attributes_with_tf_values()
          attributes {foo = #tf_type.shape<32x28x28x1>} {
  return
}

// -----

          "Shape must be fully-defined, got: shape[", dim, "] = ", shape[dim]));
    }
    num_elements *= shape[dim];
  }
  if (data.size() != num_elements) {
    return tensorflow::errors::InvalidArgument(absl::StrCat(
        "Mismatched shape and data size: \n", "Shape num_elements: ",
        num_elements, "\n", "Data size: ", data.size(), "\n"));
  }

import gradlebuild.basics.decapitalize
import gradlebuild.shade.ArtifactTypes.buildReceiptType
import gradlebuild.shade.ArtifactTypes.classTreesType
import gradlebuild.shade.ArtifactTypes.entryPointsType
import gradlebuild.shade.ArtifactTypes.manifestsType
import gradlebuild.shade.ArtifactTypes.relocatedClassesAndAnalysisType
import gradlebuild.shade.ArtifactTypes.relocatedClassesType
import gradlebuild.shade.extension.ShadedJarExtension