num_channels = 1

# Training parameters.
learning_rate = 0.001
display_step = 10
batch_size = 32

# Network parameters.
n_hidden_1 = 32  # 1st conv layer number of neurons.
n_hidden_2 = 64  # 2nd conv layer number of neurons.
n_hidden_3 = 64  # 1st fully connected layer of neurons.
flatten_size = num_features // 16 * n_hidden_2

seed = 66478


class FloatModel(tf.Module):
  """Float inference for mnist model."""

  // CHECK-SAME: %[[CONV2]]
  // CHECK-SAME: data_format = "NHWC"

  // ------------------------------------------------------------------------ //
  // Add results of convolution layers #1 and #2.
  // ------------------------------------------------------------------------ //

  %14 = "tf.AddV2"(%10, %12) : (tensor<?x256x56x56xf32>, tensor<?x256x56x56xf32>) -> tensor<?x256x56x56xf32>

  OpBuilder builder(&getContext());
  auto func_op = getOperation();

  // All TPU embedding layer related ops are annotated with
  // `_tpu_embedding_layer` attribute along with corresponding string attribute.
  // Store all tpu embedding layer related ops with value of
  // `_tpu_embedding_layer` attribute as map key.
  llvm::StringMap<Operation*> enqueue_op_map;

	ret, err := io.ReadAll(zr)
	if err != nil {
		return nil
	}
	return ret
}

// Just do the best effort.
// If an error is encountered, just return the original bytes.
// Errors will be handled upper layers.
func unboxIfPossible(origin []byte) []byte {
	b := origin
	for {
		if isValidWasmBinary(b) {
			return b
		} else if isGZ(b) {
			if b = getFileFromGZ(b); b == nil {
				return origin
			}

		return err
	}

	return nil
}

// ImageStats returns the statistics of the image.
// Notice that current logic doesn't really work for images which share layers (e.g. docker image),
// this is a known issue, and we'll address this by getting imagefs stats directly from CRI.
// TODO: Get imagefs stats directly from CRI.

	// Regression test for https://github.com/golang/go/issues/60051.

	// The length of a filename is also limited, so we can't reproduce the
	// crash by creating a single directory with a very long name; we need two
	// layers.
	a200 := strings.Repeat("a", 200)
	dirname := filepath.Join(t.TempDir(), a200, a200)

	err := os.MkdirAll(dirname, 0o700)
	if err != nil {
		t.Skipf("MkdirAll failed: %v", err)
	}
	err = os.Chdir(dirname)

        noBuildTriggered()

        when:
        sourceFile.text = "class Thing {}"

        then:
        buildTriggeredAndSucceeded()
    }

    // Just exercises the dependency management layers to shake out any weirdness
    @Requires(UnitTestPreconditions.Online)
    def "can resolve dependencies from remote repository"() {
        when:
        def sourceFile = file("src/main/java/Thing.java") << "class Thing {}"

         alt="Istio logo" title="Istio" height="100" width="100" />
</a>

---

import okio.Buffer
import okio.ForwardingSink
import okio.ForwardingSource
import okio.Sink
import okio.Source
import okio.buffer

/**
 * Transmits a single HTTP request and a response pair. This layers connection management and events
 * on [ExchangeCodec], which handles the actual I/O.
 */
class Exchange(
  internal val call: RealCall,
  internal val eventListener: EventListener,
  internal val finder: ExchangeFinder,

    add additional cost to TPU device time. Below is the logic to transform the
    kernel of shape [7, 7, 3, 64] to [4, 4, 12, 84].

    ```python
    conv0 = tf.compat.v1.layers.Conv2D(
     filters=filters,
     kernel_size=kernel_size,
     strides=2,
     padding=('SAME' if strides == 1 else 'VALID'),
     use_bias=False,
     kernel_initializer=tf.variance_scaling_initializer(),