}

// Gets the shape of operand, assuming it is a dynamic shape with static rank.
Value getShapeValue(Location loc, Value operand, PatternRewriter &rewriter) {
  RankedTensorType resultType =
      mlir::dyn_cast<RankedTensorType>(operand.getType());
  return rewriter.create<shape::ShapeOfOp>(
      loc,
      RankedTensorType::get(/*shape=*/{resultType.getRank()},

  virtual tensorflow::Status TensorHandleStatus() const;

  // Returns tensor shape. If tensor has unknown rank, shape remains untouched.
  virtual tensorflow::Status Shape(
      tensorflow::PartialTensorShape* shape) const = 0;

  // Returns tensor (full) type.
  // While there is no immediate plan to deprecate dtype and shape in favor
  // of only using full type type information, this is a future possibility.
  //

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "z" output_index: 1}
  shape {
    dim { size: 1 }
  }
  type: DT_INT32
}
fetch {
  id { node_name: "x_y_sum" }
}
fetch {
  id { node_name: "x_z_sum" }

				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[0].utilization",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[1].score",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[2].score",
				},
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "shape[3].utilization",
				},
			}),
		},
	}

		// UNC Absolute
		{`\\srv\share\long`, `\\?\UNC\srv\share\long`},
		{`//srv/share/long`, `\\?\UNC\srv\share\long`},
		{`/\srv/share/long`, `\\?\UNC\srv\share\long`},
		{`\\srv\share\long\`, `\\?\UNC\srv\share\long\`},
		{`\\srv\share\bar\.\long`, `\\?\UNC\srv\share\bar\long`},
		{`\\srv\share\bar\..\long`, `\\?\UNC\srv\share\long`},
		{`\\srv\share\bar\..\..\long`, `\\?\UNC\srv\share\long`}, // share name is not removed by ".."

// CHECK-NEXT:     subgraphs: [ {
// CHECK-NEXT:       tensors: [ {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 1,
// CHECK-NEXT:         name: "arg0",
// CHECK-NEXT:         quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:         },
// CHECK-NEXT:         has_rank: true
// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 2,
// CHECK-NEXT:         name: "arg1",

// CHECK-NEXT:     subgraphs: [ {
// CHECK-NEXT:       tensors: [ {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 1,
// CHECK-NEXT:         name: "arg0",
// CHECK-NEXT:         quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:         },
// CHECK-NEXT:         has_rank: true
// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 2,
// CHECK-NEXT:         name: "arg1",

  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;
    }
    shape.push_back(dim);
  }
  return shape;
}

}  // namespace

extern "C" {

// Converts an TensorFlow shape to the one used in MLIR.
void ConvertToMlirShape(const TensorShape& input_shape,
                        llvm::SmallVectorImpl<int64_t>* shape);

// Converts an TensorFlow shape proto to the one used in MLIR.
Status ConvertToMlirShape(const TensorShapeProto& input_shape,
                          llvm::SmallVectorImpl<int64_t>* shape);

    weight_row = array_ops.ones(
        shape=array_ops.shape(input_vocabs_placeholder), dtype=dtypes.float32
    )
    # shape: (?, 2)
    weight = array_ops.transpose_v2(
        array_ops_stack.stack([weight_row, weight_row])
    )
    # shape: (2, 2)
    output_tensor = math_ops.matmul(matmul_input, weight)

    return input_vocabs_placeholder, lookup_vals, output_tensor