std::vector<AbstractTensorHandle*> outputs(2);
    status_ =
        RunModel(MatMulGradModel, immediate_execution_ctx_.get(),
                 {A.get(), B.get()}, absl::MakeSpan(outputs), UseFunction());
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[0], dA_vals[i],
                                             /*dims*/ {3, 3},

      const float max_value = max_tensor.scalar<float>()();
      auto histogram_flat = histogram_tensor.flat<int64_t>();
      absl::Span<const int64_t> histogram_data =
          absl::MakeSpan(histogram_flat.data(), histogram_flat.size());
      id_to_collector_[ids_[idx]]->Collect(min_value, max_value,
                                           histogram_data);
    }
  }

 private:

  TF_ASSERT_OK_AND_ASSIGN(std::vector<int> constant_input_indices,
                          GetConstantInputIndicesFromContext(context_.get()));
  TF_ASSERT_OK(LockVariables(absl::MakeSpan(variables)));
  TF_ASSERT_OK_AND_ASSIGN(
      std::vector<XlaCompiler::Argument> args,
      XlaComputationLaunchContext::BuildXlaCompilerArguments(
          constant_input_indices, inputs, variables,

  Flatten(value, &args);
  std::vector<AbstractTensorHandle*> outs(
      GetFlatSize(concrete_fn.output_signature));
  TF_RETURN_IF_ERROR(
      ExecuteFunction(concrete_fn.trace, ctx, args, absl::MakeSpan(outs)));
  auto cleanup_tensors = absl::MakeCleanup([outs]() {
    for (auto t : outs) {
      t->Unref();
    }
  });
  return Unflatten(outs, concrete_fn.output_signature);
}

    Y.reset(Y_raw);
  }

  std::vector<AbstractTensorHandle*> outputs(1);
  status_ = RunModel(ReluGradModel, immediate_execution_ctx_.get(), {Y.get()},
                     absl::MakeSpan(outputs), UseFunction());
  ASSERT_EQ(errors::OK, status_.code()) << status_.message();
  ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[0], {0.0f}, /*dims*/ {},
                                           /*abs_error*/ 0));

  std::vector<VariableInfo*> variable_ptrs;
  variable_ptrs.reserve(variables.size());
  for (auto& var : variables) {
    variable_ptrs.push_back(&var);
  }
  return LockVariables(absl::MakeSpan(variable_ptrs));
}

Status SnapshotResourceVariables(OpKernelContext* ctx,
                                 absl::Span<const int> variable_indices,

    if (TF_GetCode(s) != TF_OK) {
      return;
    }
  }
  int num_outputs = unwrap(o)->expected_num_outputs;
  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Execute(
             absl::MakeSpan(reinterpret_cast<AbstractTensorHandle**>(
                                unwrap(o)->outputs.data()),
                            unwrap(o)->outputs.size()),
             &num_outputs));
}

  variable_ptrs.reserve(num_vars);
  for (int i = 0; i < num_vars; ++i) {
    variable_ptrs.push_back(&(vars[i]->var_info));
  }
  absl::Status cc_status = LockVariables(absl::MakeSpan(variable_ptrs));
  status->status = cc_status;
}

void TF_AllocateTempForVariableInfo(TF_OpKernelContext* ctx,
                                    TF_VariableInfo* var_info,

// type, and returns an expression that yields the *runtime._type
// value representing that channel type.
func MakeChanRType(pos src.XPos, n *ir.MakeExpr) ir.Node {
	assertOp(n, ir.OMAKECHAN)
	if hasRType(n, n.RType, "RType") {
		return n.RType
	}
	return chanRType(pos, n.Type())
}

// MakeMapRType asserts that n is a "make" operation for a map type,

  TF_ASSIGN_OR_RETURN(
      auto module,
      ImportSavedModel(
          model_flags.saved_model_dir(), model_flags.saved_model_version(),
          tags, absl::MakeSpan(custom_opdefs), exported_names, specs,
          !toco_flags.enable_tflite_resource_variables(), &context, &bundle));

  if (!model_flags.input_arrays().empty() ||
      !model_flags.output_arrays().empty()) {