// test indicates whether this instance of the struct was made for testing purposes.
	test bool

	bucket struct {
		// Invariants:
		// - fill >= 0
		// - capacity >= 0
		// - fill <= capacity
		fill, capacity uint64
	}
	// overflow is the cumulative amount of GC CPU time that we tried to fill the
	// bucket with but exceeded its capacity.
	overflow uint64

	// assistTimePool is the accumulated assist time since the last update.

    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
        }
        float_val: 1
      }
    }
  }
  experimental_debug_info {
  }
}
node {
  name: "gradients/Fill"
  op: "Fill"
  input: "gradients/Shape"
  input: "gradients/grad_ys_0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index_type"
    value {
      type: DT_INT32

package main

import (
	"math"
	"reflect"
)

func main() {
	for i := 0; i < 100; i++ {
		f()
		g()
	}
}

func f() {
	// Allocate map.
	m := map[float64]int{}
	// Fill to just before a growth trigger.
	const N = 13 << 4 // 6.5 * 2 * 2^k
	for i := 0; i < N; i++ {
		m[math.NaN()] = i
	}
	// Trigger growth.
	m[math.NaN()] = N

	// Iterate through map.
	i := 0
	for range m {

// CHECK:  [[CST:%.*]] = arith.constant dense<0.000000e+00> : tensor<f32>
// CHECK:  [[FILL:%.*]] = "tf.Fill"([[CONCAT]], [[CST]]) : (tensor<?xi32>, tensor<f32>) -> tensor<*xf32>
// CHECK:  [[GATHER:%.*]] = "tf.Gather"([[FILL]], %arg2) <{validate_indices = true}> : (tensor<*xf32>, tensor<i32>) -> tensor<*xf32>
// CHECK:  return [[GATHER]] : tensor<*xf32>
}

// -----

	}

	// Fill in HTTP headers
	o.fillHeaders()

	if o.Timeout <= 0 {
		o.Timeout = common.DefaultRequestTimeout
	}

	// Fill the number of calls to make.
	o.fillCallCount()

	// Fill connection parameters based on scheme and workload type.
	o.fillConnectionParams()

	// Fill in default retry options, if not specified.
	o.fillRetryOptions()

	// check must be specified
	if o.Check == nil {

  // CHECK-NEXT: %[[FULL_0:.*]] = "tf.Fill"(%[[SHAPE_0]], %[[ELEM_0]]) {ici_weight_distribution_mlir_bridge_marker = true}
  // CHECK:      %[[ELEM_1:.*]] = "tf.Const"()
  // CHECK:      {ici_weight_distribution_mlir_bridge_marker = true}
  // CHECK-NEXT: %[[SHAPE_1:.*]] = "tf.Const"()
  // CHECK:      {ici_weight_distribution_mlir_bridge_marker = true}

    %i32_min_filled = "tf.Fill" (%input_shape, %i32_min) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
    %i32_max_filled = "tf.Fill" (%input_shape, %i32_max) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
    %i32_act_max_f32_filled = "tf.Fill" (%input_shape, %i32_act_max_f32) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>

      values[dstIndex] = 0;
      entries[dstIndex] = UNSET;
    }
  }

  public void clear() {
    modCount++;
    Arrays.fill(keys, 0, size, null);
    Arrays.fill(values, 0, size, 0);
    Arrays.fill(table, UNSET);
    Arrays.fill(entries, UNSET);
    this.size = 0;
  }

  %cst = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
  %cst_0 = "tf.Const"() <{value = dense<1> : tensor<1xi64>}> : () -> tensor<1xi64>
  %fill = "tf.Fill"(%cst_0, %cst) : (tensor<1xi64>, tensor<f32>) -> tensor<1xf32>
  tf_device.replicate([%0, %fill] as %arg_r0: tensor<1xf32>) {n = 2 : i32} {
    %1 = "tf_device.launch"() <{device = "TPU_REPLICATED_HOST_0"}> ({
      %2 = "tf.Identity"(%arg_r0) : (tensor<1xf32>) -> tensor<1xf32>

      // Single byte UTF-8 characters
      // Start with "%--" and fill in the blanks
      char[] dest = new char[3];
      dest[0] = '%';
      dest[2] = UPPER_HEX_DIGITS[cp & 0xF];
      dest[1] = UPPER_HEX_DIGITS[cp >>> 4];
      return dest;
    } else if (cp <= 0x7ff) {
      // Two byte UTF-8 characters [cp >= 0x80 && cp <= 0x7ff]
      // Start with "%--%--" and fill in the blanks
      char[] dest = new char[6];