%0 = "tfl.pseudo_sparse_const"() {compressed_data = dense<[1.0, 2.0, 0.5, 0.25,...

  %0 = "tfl.pseudo_sparse_const"() {compressed_data = dense<[1.0, 2.0, 0.5, 0.25,...

//
// Eg 1. An update in canonical form:
//  * indices shape(A,B,C)
//  * updates shape(A,B,D,E,F)
// Then:
//  * D,E,F are the update window dims [2,3,4]
//  * C is the index vector dimension
//  * A,B iterate over the updates and indices
//
// If `update_window_dims` are not the trailing dimensions then updates must be
// transposed.
//
// Eg 2. An update in non-canonical form:
//  * indices shape(a,b,c)
//  * updates shape(d,e,f,a,b)

def LowerScatterNdOp :
  Pat<(TF_ScatterNdOp $indices,
       TensorOf<[AnyInteger, AnyFloat, AnyComplex]>:$updates, $shape, ConstantStrAttr<StrAttr, "">),
      (TF_TensorScatterAddOp
       (TF_FillOp $shape, (TF_ConstOp (GetScalarOfType<0> $updates))),
       $indices, $updates)>;

def LowerScatterNdOpDefaultBadIndicesPolicy :
  Pat<(TF_ScatterNdOp $indices,

      indices += absl::StrCat("[dim", dim, "]");
      count *= shape.dimensions(dim);
    }
  }
  rewrites->push_back({"{{I}}", absl::StrCat(i)});
  rewrites->push_back({"{{TYPE}}", type});
  rewrites->push_back({"{{DIM_VARS}}", absl::StrJoin(dim_vars, ", ")});
  rewrites->push_back({"{{DIM_SIZES}}", dim_sizes});
  rewrites->push_back({"{{INDICES}}", indices});

                                     ArrayRef<Value>{a, b});
}

namespace {

int64_t GetFirstIfIndicesAreContiguous(Value indices) {
  auto type = mlir::dyn_cast<RankedTensorType>(indices.getType());
  if (!type) return -1;
  auto indices_op = indices.getDefiningOp();
  if (!indices_op) return -1;
  auto const_op = llvm::dyn_cast<TF::ConstOp>(indices_op);
  if (!const_op) return -1;

  //
  // T* argN_data()
  //   Returns the buffer of type T for positional argument N.
  //
  // T& argN(...dim indices...)
  //   Returns a reference to the value of type T for positional argument N,
  //   with dim indices specifying which value. No bounds checking is performed
  //   on dim indices.

  void set_arg0_data(const void* data) {
    set_arg_data(0, data);
  }
  float* arg0_data() {

    indices = rewriter.create<mhlo::ReshapeOp>(parent_op->getLoc(),
                                               indices_type, indices);
  } else if (index_vector_dim != indices_type.getRank() - 1) {
    // If index_vector_dim isn't the last dimension in indices then it isn't
    // supported yet.
    // TODO(tberghammer): Transpose indices to support this usecase.

def LegalizeGather: Pat<
  (TF_GatherOp $params, $indices, $ignored_validate_indices),
  (TFL_GatherOp $params, $indices, ConstantAttr<I32Attr, "0">,
     ConstantAttr<I32Attr, "0">)>;

def LegalizeGatherNd : Pat<(TF_GatherNdOp $params, $indices, $bad_indices_policy),
                           (TFL_GatherNdOp $params, $indices)>;

def LegalizeGatherV2 : Pat<
  (TF_GatherV2Op $params, $indices, (Arith_ConstantOp ElementsAttr:$axis), $batch_dims),

					continue
				}
			}
			indices = append(indices, i)
		}
		if len(indices) == 0 {
			t.Errorf("%s: no error expected: %q", gotPos, gotMsg)
			continue
		}
		// len(indices) > 0

		// If there are multiple matching errors, select the one with the closest column position.
		index := -1 // index of matching error
		var delta int
		for _, i := range indices {