^bb0(%arg1: tensor<8xf32>, %arg2: tensor<f32>, %arg3: tensor<f32>):
    %2 = "tf.FakeQuantWithMinMaxVars"(%arg1, %arg2, %arg3) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
    "tfl.yield"(%2) : (tensor<8xf32>) -> ()
  }) {num_bits = 3, narrow_range = false} :  (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

// CHECK-NEXT:     } ]
// CHECK-NEXT:     signature_defs: [ ]
// CHECK-NEXT:   }

// IMPORT: "tfl.fake_quant"(%arg0) <{max = 1.400000e+00 : f32, min = 3.000000e-01 : f32, narrow_range = false, num_bits = 6 : i32}>

  %0 = "tfl.fake_quant"(%arg0) {num_bits = 6 : i32, narrow_range = false, min = 0.3:f32, max = 1.4:f32} : (tensor<4 x f32>) -> tensor<4 x f32>
  func.return %0 : tensor<4xf32>

struct ConvertStatsToQDQs : public OpRewritePattern<quantfork::StatisticsOp> {
  ConvertStatsToQDQs(int num_bits, bool narrow_range, bool is_signed,
                     bool legacy_float_scale, MLIRContext* context)
      : OpRewritePattern<quantfork::StatisticsOp>(context),
        num_bits(num_bits),
        narrow_range(narrow_range),
        is_signed(is_signed),
        legacy_float_scale(legacy_float_scale) {}

  }
}

// Sets the min / max, scale and zero_points from the fake quant num_bits
// attribute from QAT.
QuantizedType ResetMinMaxFromNumBits(const QuantizedType type,
                                     const int num_bits,
                                     const bool narrow_range,
                                     const bool is_signed) {
  if (num_bits >= 8) {
    return type;
  }

      quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

      quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

      quant_dim = input_type.getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

  let summary = [{
    Simulates the effect of uniform quantization with const range.
  }];

  let description = [{
    Given a const min, max, num_bits and narrow_range attribute, applies the
    same uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_args op. See the fakeQuantAttrsToType() utility

				s.Label = labels
			}
			if len(numLabels) > 0 {
				s.NumLabel = numLabels
				for key, units := range numUnits {
					if len(units) > 0 {
						numUnits[key] = padStringArray(units, len(numLabels[key]))
					}
				}
				s.NumUnit = numUnits
			}
		}

		s.Location = locBuffer[:len(s.locationIDX)]
		locBuffer = locBuffer[len(s.locationIDX):]
		for i, lid := range s.locationIDX {

    """Quantizes and dequantizes hist_mids using quant_min and quant_max.

    Quantization converts the range of numbers from [quant_min, quant_max] to
    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
    values greater than quant_max are converted to 2^num_bits - 1.

    The histogram represents the distribution of the data, and our goal is to
    find the quant_min and quant_max that best describe this distribution. To do