replaceMap := make(map[module.Version]module.Version, len(replacements))
	for _, r := range replacements {
		if prev, dup := replaceMap[r.Old]; dup && prev != r.New {
			base.Fatalf("go: conflicting replacements for %v:\n\t%v\n\t%v", r.Old, prev, r.New)
		}
		replaceMap[r.Old] = r.New
	}
	return replaceMap
}

// indexModFile rebuilds the index of modFile.

    rewriter.replaceOp(batch_norm, op->getResults());

    // Depending on the case, we may fuse the add, the relu, or both.
    if (!add_op || add_op.getZ().hasOneUse()) {
      // We fuse the Relu only if the add has a single use, otherwise we only
      // fuse the add itself.
      op->setAttr("activation_mode", rewriter.getStringAttr("Relu"));
      rewriter.replaceOp(relu_op, op->getResult(0));
    }

  auto reshape_op =
      InsertReshapeOp(pack_op.getLoc(), concat_op, input_type.getElementType(),
                      pack_out_shape, &rewriter);

  // Rewire output & get rid of the pack op.
  rewriter.replaceOp(pack_op, reshape_op.getResult());
  return success();
}

// ================== squared_difference ========================

LogicalResult SquaredDifference::matchAndRewrite(

      // TODO(b/267700110): Handle multiple input/output cases.
      rewriter.replaceOp(replicated_input_op, replicated_input_op.getInputs());
    } else if (auto replicated_output_op =
                   dyn_cast_or_null<TF::TPUReplicatedOutputOp>(op)) {
      // TODO(b/267700110): Handle multiple input/output cases.
      rewriter.replaceOp(replicated_output_op, replicated_output_op.getInput());
    } else {
      return failure();

      auto tf_argreduce_op = rewriter.create<ArgReduce>(
          reduce_op.getLoc(), reduce_op->getResult(1).getType(), operand,
          reduction_indices);

      rewriter.replaceOp(reduce_op, {tf_reduce_op, tf_argreduce_op});
    } else {
      auto tf_reduce_op = rewriter.create<Reduce>(
          reduce_op.getLoc(), reduce_op->getResult(0).getType(), operand,
          reduction_indices,

    auto converted = rewriter.create<TFL::CustomOp>(
        op->getLoc(), op->getResultTypes(), op->getOperands(),
        "TensorListPopBack", TFL::ConstBytesAttr::get(getContext(), ""));
    rewriter.replaceOp(op, converted.getResults());
    return success();
  }
};

struct ConvertTensorListPushBack
    : public OpRewritePattern<TF::TensorListPushBackOp> {
  using OpRewritePattern::OpRewritePattern;

          TFL::ConstBytesAttr::get(rewriter.getContext(), stringattr));
    }
  } else {
    tfl_custom.setCustomOptionAttr(
        TFL::ConstBytesAttr::get(rewriter.getContext(), ""));
  }

  rewriter.replaceOp(mhlo_custom_call, tfl_custom);
  return success();
}

std::optional<bool> IsCustomCallLegal(mhlo::CustomCallOp op) {
  if (op.getCallTargetName().starts_with("custom_call.")) {
    auto bc = op.getBackendConfig();

        num_parallel_calls_op.getY(), op.getDropRemainder(),
        batchInputOp.getF(), op.getOutputTypes(), op.getOutputShapes(),
        batchInputOp.getPreserveCardinality(), op.getMetadata());
    rewriter.replaceOp(op, {fused_op.getHandle()});
    return failure();
  }
};

#include "tensorflow/compiler/mlir/tensorflow/transforms/generated_tf_data_optimization.inc"
}  // namespace

        conv_op.getFeatureGroupCount(), conv_op.getBatchGroupCount(),
        conv_op.getPrecisionConfigAttr());
    // For static case, replace the convolution op now.
    if (!is_dynamic_broadcast) {
      rewriter.replaceOp(mul_op, {new_conv});
    } else {
      // For dynamic case, create new shape_of op and replace uses.
      shape_of_op =
          dyn_cast_or_null<mhlo::DynamicBroadcastInDimOp>(bcast_or_const_op)

        pad_op.getPaddingValue(), new_padding_low, new_padding_high,
        new_padding_interrier);

    Value orig_pad = CreateTranspose(rewriter, new_pad, transpose_perm);
    rewriter.replaceOp(pad_op, orig_pad);
    return success();
  }
};

// Transform reduce_window(transpose(x)) to transpose(reduce_window(x))
struct TransposeCommuteWithReduceWindow