inner_reshape_op->getLoc(),
        inner_reshape_input, new_reshape_shape_value);

    // Create a new reshape_op to replace the old inner reshape_op.
    rewriter.replaceOp(inner_reshape_op, new_inner_reshape_op.getResult());

    // Get the new shape for the broadcast_op after removing the extra 1s.
    llvm::SmallVector<int64_t, 6> new_broadcast_shape{

              conv_output_type, rewriter);
      output = rewriter.create<mhlo::TransposeOp>(
          conv_op.getLoc(), conv_op.getType(), output, permutation);
    }
    rewriter.replaceOp(conv_op, {output});
  }
};

// Convert a 1-D convolution into a 2-D convolution (which TF supports) so that
// it can be rewritten by the pattern `Convert2DConvOp`.

      for (auto index_result :
           llvm::zip(dynamic_indices, dynamic_shape_n.getResults())) {
        results[std::get<0>(index_result)] = std::get<1>(index_result);
      }
    }

    rewriter.replaceOp(op, results);
    return success();
  }
};

// Canonicalize ShapeNOp to ShapeOp if there is only one operand.
class ShapeNToShape : public OpRewritePattern<ShapeNOp> {

                                    ArrayRef<NamedAttribute>());
  Operation* new_unary_op = rewriter.create(new_unary_op_state);
  CopyDeviceAndUnderscoredAttributes(op, new_unary_op);

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

  return success();
}

// Hoist coefficient-wise binary operation out of the Concat op:
//
//   %0 = tf.Mul(%lhs_0, %rhs_0)
//   %1 = tf.Mul(%lhs_1, %rhs_1)

      // Make sure the ordering is the same for the pack op & unpack op.
      if (pack_input != unpack_output) return failure();
    }

    // Replace the pack's output to the unpack's input.
    rewriter.replaceOp(pack_op, input_unpack_op.getOperand());
    // At this point, we don't manually remove the redundant pack op & unpack op
    // (we cannot actually), but trust the PatterRewriter to garbage collect
    // these two ops.