input_tensor->quantization->scale.clear();
        input_tensor->quantization->zero_point.clear();
        input_tensor->quantization->min.push_back(-1.0);
        input_tensor->quantization->max.push_back(1.0);

        auto& output_tensor = sub_graph->tensors[op->outputs[0]];
        auto shape = output_tensor->shape;
        output_tensor->quantization->scale.clear();

  std::vector<TFE_TensorHandle*> compute_fn_inputs;
  TFE_TensorHandle* input_a = TestScalarTensorHandle(ctx, 2.0f);
  TFE_TensorHandle* input_b = TestScalarTensorHandle(ctx, 1.0f);
  compute_fn_inputs.push_back(input_a);
  compute_fn_inputs.push_back(input_b);

  TFE_Op* compute_fn_op = TF_ConcreteFunctionMakeCallOp(
      compute_fn, compute_fn_inputs.data(), compute_fn_inputs.size(), status);

        launch_results.push_back(result);
        launch_result_types.push_back(result.getType());
      }
    }
  }

  before ? builder->setInsertionPoint(op) : builder->setInsertionPointAfter(op);
  auto launch = builder->create<mlir::tf_device::LaunchOp>(
      op->getLoc(), builder->getStringAttr(host_device), launch_result_types);
  launch.getBody().push_back(launch_block);

    // 1.0e-6.
    if (max - min > kNearZeroTolerance) {
      effective_mins.push_back(min);
      effective_maxs.push_back(max);
    } else {
      effective_mins.push_back(std::min(min, -kSmallestHalfRange));
      effective_maxs.push_back(std::max(max, kSmallestHalfRange));
    }
  }
}

// Sets the min / max, scale and zero_points from the fake quant num_bits

        return rewriter.notifyMatchFailure(
            second, "non-concat dims have mismatching slice bounds");
    }

    new_start.push_back(first_start);
    new_limit.push_back(second_limit);
    new_slice_shape.push_back(second_limit - first_start);
  }

  auto new_slice = rewriter.create<mhlo::SliceOp>(
      FusedLoc::get(first->getContext(), {first.getLoc(), second.getLoc()}),

  EXPECT_THAT(pre_calibration_result, IsOk());

  SmallVector<func::FuncOp> func_ops;
  for (auto func_op : pre_calibration_result->getOps<func::FuncOp>()) {
    func_ops.push_back(func_op);
  }
  ASSERT_THAT(func_ops, SizeIs(2));
  EXPECT_THAT(func_ops, Contains(HasSymName("main")));
  EXPECT_THAT(func_ops, Contains(HasSymName("composite_dot_general_fn_1")));

        auto dequantize_op = builder->create<TFL::DequantizeOp>(
            op->getLoc(), dequantized_input_type, input.get());
        dequantized_inputs.push_back(dequantize_op);
      } else {
        dequantized_inputs.push_back(input.get());
      }
    }

    // Result types.
    SmallVector<Type, 4> result_types;
    for (auto result_type : op->getResultTypes()) {

        int64_t size =
            padding_low[i] + shape[i] + (shape[i] - 1) * padding_interm[i];
        padding_high.push_back(input_dim - size);
      } else {
        dims_to_reverse.push_back(i);
        padding_high.push_back(input_dim - begin_indices[i] - 1);
        padding_interm.push_back(-strides[i] - 1);

        // Pad the lower dimension up to the expected input shape.
        int64_t size =

  for (int input = 0, e = num_operands + 1; input != e; ++input) {
    if (input < dst_input) {
      state.operands.push_back(dst->getOperand(input));
    } else if (input == dst_input) {
      state.operands.push_back(source->getResult(0));
    } else {
      state.operands.push_back(dst->getOperand(input - 1));
    }
  }
  state.attributes.assign(dst->getAttrs().begin(), dst->getAttrs().end());

  min_maxs.reserve(mins.size() * 2);
  for (int i = 0, end = mins.size(); i < end; ++i) {
    llvm::APFloat min(mins[i]);
    llvm::APFloat max(maxs[i]);
    min_maxs.push_back(min);
    min_maxs.push_back(max);
  }
  // The layer stats contain only the first min/max pairs.
  mlir::ElementsAttr layer_stats = mlir::DenseFPElementsAttr::get(
      tensorflow::GetTypeFromTFTensorShape({2}, b.getF32Type()),