offset += int(buf[2])
	}

	*t = Time{}
	t.wall = uint64(nsec)
	t.ext = sec

	if offset == -1*60 {
		t.setLoc(&utcLoc)
	} else if _, localoff, _, _, _ := Local.lookup(t.unixSec()); offset == localoff {
		t.setLoc(Local)
	} else {
		t.setLoc(FixedZone("", offset))
	}

	return nil
}

// TODO(rsc): Remove GobEncoder, GobDecoder, MarshalJSON, UnmarshalJSON in Go 2.

		name, offset, _, _, _ := local.lookup(t.unixSec())
		if offset == zoneOffset && (zoneName == "" || name == zoneName) {
			t.setLoc(local)
			return t, nil
		}

		// Otherwise create fake zone to record offset.
		zoneNameCopy := stringslite.Clone(zoneName) // avoid leaking the input value
		t.setLoc(FixedZone(zoneNameCopy, zoneOffset))
		return t, nil
	}

	if zoneName != "" {

    return failure();
  }

  for (Operation& inner_op : quantized_func.getBody().front().getOperations()) {
    if (!inner_op.hasAttr(kAttrMapAttribute)) continue;
    inner_op.setLoc(main_op->getLoc());
  }
  return success();
}

// Get the corresponding quantized function name from the given function name.
std::string GetQuantizedFunctionName(StringRef func_name,

          if (TF::TensorListSetItemOp set_op =
                  llvm::dyn_cast<TF::TensorListSetItemOp>(use.getOwner())) {
            element_shape = rewriter.create<TF::ShapeOp>(
                op.getLoc(),
                tensorflow::GetTypeFromTFTensorShape({-1}, shape_dtype),
                set_op.getItem());
            element_shape_acquired = true;

    auto min_scaled =
        rewriter.create<DivOp>(op.getLoc(), float_min, quant_to_float);
    auto min_scaled_sub =
        rewriter.create<SubOp>(op.getLoc(), quant_min, min_scaled);

    auto mid_rounded =
        rewriter.create<RoundOp>(op.getLoc(), scalar_ty, min_scaled_sub);

    auto nudged_zero_point_val = rewriter.create<ClipByValueOp>(
        op.getLoc(), scalar_ty, mid_rounded, quant_min, quant_max);

  builder.create<mlir::TF::YieldOp>(if_region.getLoc(),
                                    /*operands=*/ArrayRef<Value>{});

  // Create empty else branch region.
  auto& else_branch = host_side_if.getElseBranch();
  else_branch.push_back(new Block);
  builder.setInsertionPointToEnd(&else_branch.front());
  builder.create<mlir::TF::YieldOp>(if_region.getLoc(),

        rewriter.create<arith::ConstantOp>(op->getLoc(), type, begin_attr);
    auto end_attr = DenseElementsAttr::get<int32_t>(type, padded_end);
    auto end_op =
        rewriter.create<arith::ConstantOp>(op->getLoc(), type, end_attr);
    auto stride_attr = DenseElementsAttr::get<int32_t>(type, padded_stride);
    auto stride_op =
        rewriter.create<arith::ConstantOp>(op->getLoc(), type, stride_attr);

        CreateI8F32UniformQuantizedType(
            uniform_quantize_call_op.getLoc(), *rewriter.getContext(),
            input_scale_value, input_zero_point_value);
    auto input_uniform_quantize_op =
        rewriter.create<stablehlo::UniformQuantizeOp>(
            uniform_quantize_call_op.getLoc(),
            /*result=*/
            mlir::cast<TensorType>(input_value.getType())

    new_operands[index] = builder.create<TF::ReadVariableOp>(
        caller->getLoc(), ArrayRef<Type>{new_type}, ArrayRef<Value>{operand});
    caller->setOperand(index, new_operands[index]);
    if (updated_index < 0) continue;
    builder.setInsertionPointAfter(caller);
    builder.create<TF::AssignVariableOp>(
        caller->getLoc(), ArrayRef<Type>{},
        ArrayRef<Value>{operand, caller->getResult(updated_index)});
  }

                                                    TypeRange(types), result);
      new_forward_ops.insert(replicated_output);
      // TODO(bfontain): Check for other attributes.
      replicated_output->setAttr(kDevice, builder.getStringAttr(""));
      TF::TPUReplicatedInputOp input = builder.create<TF::TPUReplicatedInputOp>(