Value start_op = rewriter.create<TF::ConstOp>(iota_op.getLoc(), start);
    Value limit_op = rewriter.create<TF::ConstOp>(iota_op.getLoc(), limit);
    Value delta_op = rewriter.create<TF::ConstOp>(iota_op.getLoc(), delta);
    Value result = rewriter.create<TF::RangeOp>(iota_op.getLoc(), range_type,
                                                start_op, limit_op, delta_op);

        filter_constant_op->getLoc(), *rewriter.getContext(),
        filter_quantized_type.getScale(), filter_quantized_type.getZeroPoint(),
        /*narrow_range=*/true);
  }

  // Required because the quantized dimension is changed from 3 -> 0.
  auto new_filter_result_type = RankedTensorType::getChecked(
      filter_constant_op.getLoc(), /*shape=*/new_filter_shape,

      }
    } else {
      bias = rewriter
                 .create<AddOp>(add_op.getLoc(), bias, constant_val,
                                rewriter.getStringAttr("NONE"))
                 .getOutput();
    }

    auto fc = rewriter.create<TFL::FullyConnectedOp>(
        FusedLoc::get(fc_op.getContext(), {fc_op.getLoc(), add_op.getLoc()}),
        add_op.getType(),
        /*input=*/fc_op.getInput(),

  auto transpose = dyn_cast_or_null<TF::TransposeOp>(op.getX().getDefiningOp());
  if (!transpose) return {};

  // If the transpose ops are on different devices, we don't fold them.
  if (transpose->getBlock() != op->getBlock()) {
    tensorflow::DataType dtype;
    auto status = tensorflow::ConvertToDataType(
        op.getX().getType().cast<TensorType>().getElementType(), &dtype);
    if (status.ok()) {

  if (!data_format_is_valid) {
    return emitOptionalError(op.getLoc(), "Invalid data format provided");
  }

  const StringRef paddings = op.getPadding();
  tensorflow::Padding padding;
  auto padding_is_valid = GetPaddingFromString(paddings.str(), &padding);
  if (!padding_is_valid.ok()) {
    return emitOptionalError(op.getLoc(), "Invalid padding format provided");
  }

  // Verifies that,

    // We make use of a separate builder to insert the source at the top of
    // the block.
    mlir::OpBuilder builder_at_begin(builder_.getBlock(),
                                     builder_.getBlock()->begin());
    auto source_op =
        builder_at_begin.create<mlir::tf_executor::NextIterationSourceOp>(
            loc, operands[0].getType(), result.attributes);

		writeErrorResponseJSON(ctx, w, errorCodes.ToAPIErr(ErrNotImplemented), r.URL)
		return
	}

	nsLock := objectAPI.NewNSLock(minioMetaBucket, "site-net-perf")
	lkctx, err := nsLock.GetLock(ctx, globalOperationTimeout)
	if err != nil {
		writeErrorResponseJSON(ctx, w, errorCodes.ToAPIErr(toAPIErrorCode(ctx, err)), r.URL)
		return
	}
	ctx = lkctx.Context()
	defer nsLock.Unlock(lkctx)

    YieldOp yield_op = GetYield();
    // In order to correctly round-trip, we can only use this syntax when all
    // the locations are identical.
    if (controlled_op.getLoc() == getLoc() && yield_op.getLoc() == getLoc()) {
      p << " controls ";
      p.printGenericOp(&controlled_op);
      return;
    }
  }
  p << ' ';
  p.printRegion(getOperation()->getRegion(0));

	}

	// Lock the object name before starting replication operation.
	// Use separate lock that doesn't collide with regular objects.
	lk := objectAPI.NewNSLock(bucket, "/[replicate]/"+dobj.ObjectName)
	lkctx, err := lk.GetLock(ctx, globalOperationTimeout)
	if err != nil {
		globalReplicationPool.queueMRFSave(dobj.ToMRFEntry())
		sendEvent(eventArgs{
			BucketName: bucket,
			Object: ObjectInfo{
				Bucket:       bucket,

          tl_element_shape.replaceAllUsesWith(initial_element_shape);
        } else {
          OpBuilder b(use.getOwner());
          Operation* cast_op = InsertCast(
              b, use.getOwner()->getLoc(),
              tl_element_shape.getResult().getType(), initial_element_shape);
          if (!cast_op) return false;
          tl_element_shape.replaceAllUsesWith(cast_op->getResult(0));
        }
        continue;