);
}

def TFL_ReduceAnyOp : TFL_Op<"reduce_any", [Pure]> {
  let summary = [{
Computes the "logical or" of elements across dimensions of a tensor.
  }];

  let description = [{
Reduces `input` along the dimensions given in `axis`. Unless
`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
`axis`. If `keep_dims` is true, the reduced dimensions are
retained with length 1.
  }];

		DeleteReplication: drs,
		Versioned:         globalBucketVersioningSys.PrefixEnabled(bucket, dobj.ObjectName),
		// Objects matching prefixes should not leave delete markers,
		// dramatically reduces namespace pollution while keeping the
		// benefits of replication, make sure to apply version suspension
		// only at bucket level instead.
		VersionSuspended: globalBucketVersioningSys.Suspended(bucket),
	})

  // Return the held attribute value.
  return getValue();
}

// Builds a constant op with the specified attribute `value`. The result
// op's type is deduced from `value`; if `value` is of scalar type,
// wraps it up with a tensor type of empty shape.
// TODO(jpienaar): This one differs from the autogenerated one as it takes an

odes:function(e){this.inflight+=e.length;for(var t,n=0,r=e.length;n<r&&(t=e[n]);n++)this.require(t);this.checkDone()},addNode:function(e){this.inflight++,this.require(e),this.checkDone()},require:function(e){var t=e.src||e.href;e.__nodeUrl=t,this.dedupe(t,e)||this.fetch(t,e)},dedupe:function(e,t){if(this.pending[e])return this.pending[e].push(t),!0;return this.cache[e]?(this.onload(e,t,this.cache[e]),this.tail(),!0):(this.pending[e]=[t],!1)},fetch:function(e,r){if(n.load&&console.log("fetch",e,r...

// The invocations in this call are expected to tear down all pod resources.
// When this method exits the pod is expected to be ready for cleanup. This method
// reduces the latency of pod cleanup but is not guaranteed to get called in all scenarios.
//
// Because the kubelet has no local store of information, all actions in this method that modify

          {static_cast<int>(return_squeeze_shape.size())},
          mlir::IntegerType::get(value_tensor.getContext(), 32)),
      llvm::ArrayRef(return_squeeze_shape));
}

// This is a utility function to deduce the effective permutation to apply on
// TFL_TransposeOp when the tensor has some dimensions with value==1
// Example- "tfl.transpose"(tensor<56x8x56x1x1x1x7xf32>, [4, 5, 1, 2, 0, 6, 3])

  flags+=" --proxy-mode=kernelspace"

  # Configure kube-proxy to run as a windows service.
  flags+=" --windows-service=true"

  # Enabling Windows DSR mode unlocks newer network features and reduces
  # port usage for services.
  # https://techcommunity.microsoft.com/t5/networking-blog/direct-server-return-dsr-in-a-nutshell/ba-p/693710
  if [[ "${WINDOWS_ENABLE_DSR:-}" == "true" ]]; then

		p.HFiles,
		p.FFiles,
		p.SFiles,
		p.SwigFiles,
		p.SwigCXXFiles,
		p.SysoFiles,
		p.TestGoFiles,
		p.XTestGoFiles,
	)

	// EmbedFiles may overlap with the other files.
	// Dedup, but delay building the map as long as possible.
	// Only files in the current directory (no slash in name)
	// need to be checked against the files variable above.
	var have map[string]bool

  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::tf_saved_model::CreateDedupBoundInputBindingPass());
  if (mlir::failed(pm.run(module)))
    return diag_handler.Combine(
        errors::Internal("Failed to dedup bound inputs."));

  return absl::OkStatus();
}

}  // namespace

SavedModelMLIRImportInput::~SavedModelMLIRImportInput() {}

Cause out of natural Philosophy, feigning Hypotheses for explaining all things mechanically, and referring other Causes to Metaphysicks: Whereas the main Business of natural Philosophy is to argue from Phænomena without feigning Hypotheses, and to deduce Causes from Effects, till we come to the very first Cause, which certainly is not mechanical; and not only to unfold the Mechanism of the World, but chiefly to resolve these and such like Questions. What is there in places almost empty of Matter,...