"""
    in_placeholder = array_ops.placeholder(dtypes.float32, shape=input_shape)

    filters = random_ops.random_uniform(
        shape=filter_shape, minval=-1.0, maxval=1.0
    )
    if use_variable_for_filter:
      filters = variables.Variable(filters)

    output_tensor = nn_ops.conv2d(
        in_placeholder,
        filters,
        strides=[1, 1, 2, 1],

  bool force_graph_mode_calibration = 14;

  // Defines calibration options for quantization. This option is only used for
  // activation of static range quantization (SRQ). Quantization calibration
  // method is set to MIN_MAX by default.
  stablehlo.quantization.CalibrationOptions calibration_options = 15;

  // Configuration related to quantization debugger.
  stablehlo.quantization.DebuggerConfig debugger_config = 16;

  reserved 3;

  let description = [{
The generated values are uniform integers in the range `[minval, maxval)`.
The lower bound `minval` is included in the range, while the upper bound
`maxval` is excluded.

The random integers are slightly biased unless `maxval - minval` is an exact
power of two.  The bias is small for values of `maxval - minval` significantly
smaller than the range of the output (either `2^32` or `2^64`).
  }];

CACHED_FORMATTERS={};function getNumberFormatter(minSpec,maxSpec,minCtx,maxCtx){const key=minSpec+'-'+maxSpec+'-'+minCtx+'-'+maxCtx;let formatter=CACHED_FORMATTERS[key];if(formatter===undefined){let minimumFractionDigits=minCtx!==undefined?minCtx:minSpec;let maximumFractionDigits=maxCtx!==undefined?maxCtx:maxSpec;if(minimumFractionDigits>maximumFractionDigits){if(minCtx!==undefined&&maxCtx===undefined){maximumFractionDigits=minimumFractionDigits;}else if(minCtx===undefined&&maxCtx!==undefined){minimumFractionD...

  """A model with 2 signatures.

  Used to test where the quantizer has to handle multiple signatures.
  """

  def __init__(self):
    self.matmul_filters = random_ops.random_uniform(
        shape=(4, 3), minval=-1.0, maxval=1.0
    )
    self.conv_filters = np.random.uniform(
        low=-10, high=10, size=(2, 3, 3, 2)
    ).astype('f4')

  @def_function.function(
      input_signature=[

#define PosInf   0x7FF0000000000000
#define NaN      0x7FF8000000000001
#define NegInf   0xFFF0000000000000
#define PosOne   0x3FF0000000000000
#define NegOne   0xBFF0000000000000
#define NegZero  0x8000000000000000

// Minimax polynomial approximation
DATA ·powrodataL51<> + 0(SB)/8, $-1.0
DATA ·powrodataL51<> + 8(SB)/8, $1.0
DATA ·powrodataL51<> + 16(SB)/8, $0.24022650695910110361E+00
DATA ·powrodataL51<> + 24(SB)/8, $0.69314718055994686185E+00

  Output shape_shape = ops::Const(root.WithOpName("shape_shape"), {2}, {1});
  Output shape =
      ops::RandomUniformInt(root.WithOpName("shape"), shape_shape,
                            ops::Const(root.WithOpName("minval"), 1),
                            ops::Const(root.WithOpName("maxval"), 20));
  Output reshape_input =
      ops::Placeholder(root.WithOpName("reshape_input"), DT_FLOAT,

    val kotlinCoroutines = "org.jetbrains.kotlinx:kotlinx-coroutines-core"
    val kotlinCoroutinesDebug = "org.jetbrains.kotlinx:kotlinx-coroutines-debug"
    val littleproxy = "xyz.rogfam:littleproxy"
    val mina = "org.apache.mina:mina-core"
    val mockitoCore = "org.mockito:mockito-core"
    val mockitoKotlin = "com.nhaarman:mockito-kotlin"
    val mockitoKotlin2 = "com.nhaarman.mockitokotlin2:mockito-kotlin"

// RUN: tf-quant-opt %s -quant-insert-custom-aggregation-ops='test-case=MIN_MAX' -split-input-file | FileCheck --check-prefix=MIN-MAX-CHECK %s
// RUN: tf-quant-opt %s -quant-insert-custom-aggregation-ops='test-case=AVERAGE_MIN_MAX'  -split-input-file | FileCheck --check-prefix=AVERAGE-MIN-MAX-CHECK %s
// RUN: tf-quant-opt %s -quant-insert-custom-aggregation-ops='test-case=HISTOGRAM_PERCENTILE' -split-input-file | FileCheck --check-prefix=HISTOGRAM-PERCENTILE-CHECK %s

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         </artifact>
      </component>
      <component group="org.apache.mina" name="mina-core" version="2.0.17">
         <artifact name="mina-core-2.0.17.jar">
            <pgp value="4D2DB2916149BAA9D0C92F3731474E5E7C6B7034"/>
         </artifact>
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