"go/token"
	. "internal/types/errors"
	"strings"
)

// funcInst type-checks a function instantiation.
// The incoming x must be a generic function.
// If ix != nil, it provides some or all of the type arguments (ix.Indices).
// If target != nil, it may be used to infer missing type arguments of x, if any.
// At least one of T or ix must be provided.
//
// There are two modes of operation:
//

// -----

// CHECK-LABEL: @gather_v2
//  CHECK-SAME: %[[PARAMS:[a-zA-Z0-9_]+]]
//  CHECK-SAME: %[[INDICES:[a-zA-Z0-9_]+]]
func.func @gather_v2(%params: tensor<16x2x3xf32>, %indices: tensor<16x5xi32>) -> tensor<16x2x5xf32> {
  //      CHECK: mhlo.torch_index_select
  // CHECK-SAME:   %[[PARAMS]], %[[INDICES]]
  // CHECK-SAME:   batch_dims = 1
  // CHECK-SAME:   dim = 2

		smap := makeSubstMap(tparams, targs)
		params = check.subst(nopos, params, smap, nil, check.context()).(*Tuple)
	}

	// Unify parameter and argument types for generic parameters with typed arguments
	// and collect the indices of generic parameters with untyped arguments.
	// Terminology: generic parameter = function parameter with a type-parameterized type
	u := newUnifier(tparams, targs, check.allowVersion(pos, go1_21))

        // this loop by index is required to avoid possible ConcurrentModificationException
        val annotations = annotationContainer.resolvedCompilerRequiredAnnotations(firSymbol)
        for (index in annotations.indices) {
            val annotation = annotations[index]
            if (annotation.toAnnotationClassIdSafe(useSiteSession) == classId) {
                return true
            }
        }

        false

		smap := makeSubstMap(tparams, targs)
		params = check.subst(nopos, params, smap, nil, check.context()).(*Tuple)
	}

	// Unify parameter and argument types for generic parameters with typed arguments
	// and collect the indices of generic parameters with untyped arguments.
	// Terminology: generic parameter = function parameter with a type-parameterized type
	u := newUnifier(tparams, targs, check.allowVersion(posn, go1_21))

    for (const auto& phase : group.second) {
      for (const auto& pass : phase.second) {
        // Iterate over the pass_names_ and insert the pass pointer at all the
        // corresponding indices in the pass_ids vector.
        auto iter = pass_names.begin();
        while ((iter = std::find(iter, pass_names.end(), pass->name())) !=
               pass_names.end()) {

  EXPECT_THAT(subgraph->tensors[0]->quantization->zero_point, IsEmpty());

  // Testing output (indices) tensor type and ensuring it
  // was not quantized
  EXPECT_THAT(subgraph->tensors[1]->type, Eq(TensorType_INT64));
  EXPECT_THAT(subgraph->tensors[1]->name, Eq("indices"));
  EXPECT_THAT(subgraph->tensors[1]->quantization->scale, IsEmpty());

// followed by that number of elements.
//
// The types below correspond to the encoded data structure in the
// object file.

// Symbol indexing.
//
// Each symbol is referenced with a pair of indices, { PkgIdx, SymIdx },
// as the symRef struct above.
//
// PkgIdx is either a predeclared index (see PkgIdxNone below) or
// an index of an imported package. For the latter case, PkgIdx is the

// file name incorporates the meta-data hash), and add the counter
// data file to the appropriate pod.
//
// This process is complicated by the fact that we need to keep track
// of directory indices for counter data files. Here is an example to
// motivate:
//
//	directory 1:
//
// M1   covmeta.9bbf1777f47b3fcacb05c38b035512d6
// C1   covcounters.9bbf1777f47b3fcacb05c38b035512d6.1677673.1662138360208416486

  }];

  let arguments = (ins
    Arg<TF_ResourceTensor, "", [TF_VariableRead]>:$resource,
    TF_I32OrI64Tensor:$indices
  );

  let results = (outs
    TF_Tensor:$output
  );

  TF_DerivedOperandTypeAttr Tindices = TF_DerivedOperandTypeAttr<1>;
  TF_DerivedResultTypeAttr dtype = TF_DerivedResultTypeAttr<0>;
}