Remove-Item $zulu_zip; \
  $env:PATH = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\") + \";${zulu_root}\\bin\"; \
  [Environment]::SetEnvironmentVariable(\"PATH\", $env:PATH, \"Machine\"); \
  $env:JAVA_HOME = $zulu_root; \
  [Environment]::SetEnvironmentVariable(\"JAVA_HOME\", $env:JAVA_HOME, \"Machine\")

# Point to the LLVM installation.
# The Bazel Windows guide claims it can find LLVM automatically,

  Remove-Item $zulu_zip; \
  $env:PATH = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\") + \";${zulu_root}\\bin\"; \
  [Environment]::SetEnvironmentVariable(\"PATH\", $env:PATH, \"Machine\"); \
  $env:JAVA_HOME = $zulu_root; \
  [Environment]::SetEnvironmentVariable(\"JAVA_HOME\", $env:JAVA_HOME, \"Machine\")

# Point to the LLVM installation.
# The Bazel Windows guide claims it can find LLVM automatically,

                                    // If we don't clean that up there might be leftover json files from other report builds running on the same machine.
                                    """
                                    results/performance/build/test-results-*.zip!performance-tests/report/css/*.css => $performanceResultsDir/

  fi
fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,
# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

			// spurious blank caused by Split on "\n"
			output = output[1:]
			continue
		}
		t.Errorf("unexpected output: %q", output[0])
		output = output[1:]
	}

	// Checked printing.
	// Now check machine code layout.

	top := pList.Firstpc
	var text *obj.LSym
	ok = true
	ctxt.DiagFunc = func(format string, args ...interface{}) {
		t.Errorf(format, args...)
		ok = false
	}

# LINUX ARM64 PYCPP
# In Linux Arm64 presubmit/continuous build, we cross-compile the binaries on
# Linux x86 so that we can use RBE. Since tests still need to run on the single
# host Arm64 machine, the build becomes too slow (~30 min) to be a presubmit.
# For testing purposes, we want to see the runtime performance of an
# experimental job that is build-only, i.e, we only build the test targets and

    "article"
);
suggester.indexer().indexFromDocument(reader, 4, 50);
```

### Search Analytics

```java
// Track user queries for analytics
QueryLog userQuery = new QueryLog("machine learning tutorials", "user456");
suggester.indexer().indexFromQueryLog(userQuery);

// Get trending searches
PopularWordsResponse trending = suggester.popularWords()
    .setSize(10)
    .execute()

</p>

<p>
Reads of memory locations larger than a single machine word
are encouraged but not required to meet the same semantics
as word-sized memory locations,
observing a single allowed write <i>w</i>.
For performance reasons,
implementations may instead treat larger operations
as a set of individual machine-word-sized operations
in an unspecified order.

def is_linux():
  return platform.system() == 'Linux'


def is_macos():
  return platform.system() == 'Darwin'


def is_ppc64le():
  return platform.machine() == 'ppc64le'


def is_s390x():
  return platform.machine() == 's390x'


def is_cygwin():
  return platform.system().startswith('CYGWIN_NT')


def get_input(question):
  try:
    try:
      answer = raw_input(question)