Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

func newStreamHandler[Payload, Req, Resp RoundTripper](h HandlerID) *StreamTypeHandler[Payload, Req, Resp] {
	return &StreamTypeHandler[Payload, Req, Resp]{id: h, InCapacity: 1, OutCapacity: 1}
}

// Register a handler for two-way streaming with payload, input stream and output stream.
// An optional subroute can be given. Multiple entries are joined with '/'.

  s/getMojoConfiguration()/getConfiguration()/g

  That should re-enable usage of mojo configuration. Ideally, we won't need any
  of the code that references this method, since the container should provide
  some way of recontextualizing the mojo, and all we would need to do is inject
  POM configuration via the lookup method or something.

* PluginDescriptorBuilder

  s/getMojoConfiguration()/getConfiguration()/g

 * href="https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)">graph</a>-structured data.
 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * topological sort) that only need a way of accessing the predecessors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * {@snippet :

 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * breadth first traversal) that only need a way of accessing the successors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * {@snippet :
 * public <N> someGraphAlgorithm(N startNode, SuccessorsFunction<N> successorsFunction);

   * or extract a variable, and put the suppression on that. However, a local variable typically
   * doesn't work: Because nullness analyses typically infer the nullness of local variables,
   * there's no way to assign a {@code @Nullable T} to a field {@code T foo;} and instruct the
   * analysis that that means "plain {@code T}" rather than the inferred type {@code @Nullable T}.

    assertTrue(multimap.keySet() instanceof SortedSet);
    assertTrue(multimap.asMap() instanceof SortedMap);
  }

  // J2kt cannot translate the Comparable rawtype in a usable way (it becomes Comparable<Object>
  // but types are typically only Comparable to themselves).
  @SuppressWarnings({"rawtypes", "unchecked"})
  private static MultimapBuilderWithKeys<Comparable<?>> rawtypeToWildcard(

   * or extract a variable, and put the suppression on that. However, a local variable typically
   * doesn't work: Because nullness analyses typically infer the nullness of local variables,
   * there's no way to assign a {@code @Nullable T} to a field {@code T foo;} and instruct the
   * analysis that that means "plain {@code T}" rather than the inferred type {@code @Nullable T}.

  xsi:schemaLocation="http://maven.apache.org/TOOLCHAINS/1.1.0 http://maven.apache.org/xsd/toolchains-1.1.0.xsd">

  <!--
   | With toolchains you can refer to installations on your system. This
   | way you don't have to hardcode paths in your pom.xml.
   |
   | Every toolchain consist of 3 elements:
   | * type: the type of tool. An often used value is 'jdk'. Toolchains-aware

// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package grid

import (
	"context"
	"errors"
)

// A Stream is a two-way stream.
// All responses *must* be read by the caller.
// If the call is canceled through the context,
// the appropriate error will be returned.
type Stream struct {
	// responses from the remote server.