// Force all constants to be on the host memory.
      TF_RETURN_IF_ERROR(GetBodyAndConstantsAndResources(
          flr, function, &fbody, &constant_arg_indices, &resource_arg_indices));
      VLOG(3) << "Found constant arg indices: "
              << absl::StrJoin(constant_arg_indices, ", ");

      node->AddAttr("_input_hostmem", constant_arg_indices);
    }
  }
  return absl::OkStatus();
}

	trustAD       bool          // add AD flag to queries
	noReload      bool          // do not check for config file updates
}

// serverOffset returns an offset that can be used to determine
// indices of servers in c.servers when making queries.
// When the rotate option is enabled, this offset increases.
// Otherwise it is always 0.
func (c *dnsConfig) serverOffset() uint32 {
	if c.rotate {

                "of indices");
    }
  }

  if ((indices_rank == 0) || (indices_rank == batch_dims_i)) {
    // Scalar indices (output is rank(params) - 1).
    // Erase shape[axis]
    shape.erase(shape.begin() + axis_i);
  } else if (indices_rank == 1) {
    // Vector indices (output is rank(params)).
    // Copy indices.shape into params.shape[axis]

    $constant_operand_indices are the indices of the inputs that are constant to the TPU program (e.g. weights in inference), the rest of the inputs are input tensors.
    constant_operand_indices is sorted in ascending order.
    $operands_with_static_shape are indices of operands that are tagged with a maximum static shape.

	}
	indexSchema, err := schema.Parse(&IndexTest{}, &sync.Map{}, schema.NamingStrategy{})
	if err != nil {
		t.Fatalf("failed to parse user index, got error %v", err)
	}
	indices := indexSchema.ParseIndexes()
	CheckIndices(t, map[string]schema.Index{
		"idx_index_tests_field_a": {
			Name:   "idx_index_tests_field_a",

				// best position for error reporting
				pos := ix.Pos()
				if i < len(ix.Indices) {
					pos = ix.Indices[i].Pos()
				}
				check.softErrorf(atPos(pos), InvalidTypeArg, "%v", err)
			} else {
				check.mono.recordInstance(check.pkg, ix.Pos(), inst.TypeParams().list(), inst.TypeArgs().list(), ix.Indices)
			}
		}

		// TODO(rfindley): remove this call: we don't need to call validType here,

    the indices that are previously set in _TfrtSetResource op.
    `shared_name` and `container` are used to store identifying information for
    resource tensors that have been hoisted. This is important for resource
    alias analysis and side effect analysis. These are empty for non-resource
    type tensors.
  }];

  let arguments = (ins
    I64ArrayAttr:$indices,
    StrArrayAttr:$shared_name,

          loc, indices, swap_index, scalar_one);

      // Then perform the swap.
      // indices[i] <- indices[swaps[i]]
      indices = builder->create<mhlo::DynamicUpdateSliceOp>(
          loc, indices.getType(), indices, target_index, llvm::ArrayRef(i));
      // indices[swaps[i]] <- indices[i]
      indices = builder->create<mhlo::DynamicUpdateSliceOp>(

            List<Integer> indices = new ArrayList<>();
            List<String> tgt = target.getModules();
            Set<String> excludes = new LinkedHashSet<>(tgt);
            List<String> merged = new ArrayList<>(tgt.size() + src.size());
            merged.addAll(tgt);
            for (int i = 0, n = tgt.size(); i < n; i++) {
                indices.add(i);
            }

// the value of the variable at the time of lowering/compilation)}.
//
// The obtained PjRtBuffers are populated to `args` vector.
// `non_donatable_input_indices` will also be set, which contains the indices of
// the input that should not be donated to output.
//
// There can be three types of input: 1. Tensor with PjRtTensorBuffer; 2.
// Tensor with AsyncValueTensor; 3. Tensor with raw device mem pointer.