import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_POPULATION_VARIANCE;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_STATS;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_POPULATION_VARIANCE;

	MOVQ 3395469782(R10), R8        // ERROR "offset too large"
	LEAQ 3395469782(AX), AX         // ERROR "offset too large"
	ADDQ 3395469782(AX), AX         // ERROR "offset too large"
	ADDL 3395469782(AX), AX         // ERROR "offset too large"
	ADDW 3395469782(AX), AX         // ERROR "offset too large"
	LEAQ 433954697820(AX), AX       // ERROR "offset too large"

import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_POPULATION_VARIANCE;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_STATS;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_POPULATION_VARIANCE;

        - label: "Does this affect error handling?"
        - label: "Is this about generics?"
        - label: "Is this change backward compatible? Breaking the Go 1 compatibility guarantee is a large cost and requires a large benefit"

  - type: textarea
    id: related-proposals
    attributes:
      label: Has this idea, or one like it, been proposed before?
      description: If so, how does this proposal differ?

 */

package model

import java.util.LinkedList
import kotlin.math.min

/**
 * Split a list of elements into nearly even sublist. If an element is too large, largeElementSplitFunction will be used to split the large element into several smaller pieces;
 * if some elements are too small, they will be aggregated by smallElementAggregateFunction.
 *
 * @param list the list to split, must be ordered by size desc

// A chunk has the following format:
//
//	<chunk-size-as-hex> + ";chunk-signature=" + <signature-as-hex> + "\r\n" + <payload> + "\r\n"
//
// First, we read the chunk size but fail if it is larger
// than 16 MiB. We must not accept arbitrary large chunks.
// One 16 MiB is a reasonable max limit.
//
// Then we read the signature and payload data. We compute the SHA256 checksum

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems

      assertThrows<IllegalArgumentException> {
        checkDuration(
          "timeout",
          1L + Int.MAX_VALUE.toLong(),
          TimeUnit.MILLISECONDS,
        )
      },
    ).hasMessage("timeout too large")
  }

  @Test
  fun testDurationDuration() {
    assertThat(checkDuration("timeout", 0.milliseconds)).isEqualTo(0)
    assertThat(checkDuration("timeout", 1.milliseconds)).isEqualTo(1)

    assertThat(

  // +optional
  optional string selectedNode = 1;

  // PotentialNodes lists nodes where the Pod might be able to run.
  //
  // The size of this field is limited to 128. This is large enough for
  // many clusters. Larger clusters may need more attempts to find a node
  // that suits all pending resources. This may get increased in the
  // future, but not reduced.
  //
  // +listType=set
  // +optional

from fastapi import FastAPI
from fastapi.responses import FileResponse

some_file_path = "large-video-file.mp4"
app = FastAPI()


@app.get("/", response_class=FileResponse)
async def main():