biasadd_op = llvm::cast<TF::BiasAddOp>(consumer_op);
      final_op_is_bts = false;
    }
  } else {
    return rewriter.notifyMatchFailure(
        consumer_op, "next op is neither BiasAdd nor BatchToSpaceND");
  }

  std::optional<ArrayAttr> dilations_attr = ExtractDilationsAttrFromBlockShape(
      stb_op.getBlockShape(), bts_op.getBlockShape(), expand_axis, rewriter);
  if (!dilations_attr.has_value()) {

  // CHECK: return %[[DOT]]
}

// -----

// Test that q/dq pair is not inserted between constant and XlaCallModule op
// whose entry function name does not include conv nor dot_general.

func.func @no_qdq_except_conv_and_dot_general(%arg0: tensor<2x3x2xi64>) -> tensor<2x3x2x2xf32> attributes {tf._original_func_name = "main_0"} {

			}
		}

		if len(localPartBytes) == 0 {
			return mailbox, false
		}

		// From RFC 3696, Section 3:
		// “period (".") may also appear, but may not be used to start
		// or end the local part, nor may two or more consecutive
		// periods appear.”
		twoDots := []byte{'.', '.'}
		if localPartBytes[0] == '.' ||
			localPartBytes[len(localPartBytes)-1] == '.' ||
			bytes.Contains(localPartBytes, twoDots) {

    }
    return new RegularImmutableMap<K, V>(hashTable, alternatingKeysAndValues, n);
  }

  /**
   * Returns a hash table for the specified keys and values, and ensures that neither keys nor
   * values are null. This method may update {@code alternatingKeysAndValues} if there are duplicate
   * keys. If so, the return value will indicate how many entries are still valid, and will also

    ./gradlew :distributions-full:binDistributionZip

This will create a minimal distribution at `subprojects/distributions-full/build/distributions/gradle-<version>-bin.zip`, just what's needed to run Gradle (i.e. no sources nor docs).

You can then use it as a Gradle Wrapper local distribution in a Gradle based project by using a `file:/` URL pointing to the built distribution:

copyright notice, this list of conditions and the following disclaimer
in the documentation and/or other materials provided with the
distribution.
   * Neither the name of Google Inc. nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS

namespace tensorflow {

// Default PaddedShapeFn implementation that simply returns the unpadded
// on-device shape. This is accurate for CPU and GPU devices that neither
// transpose nor pad tensors.
Status DefaultPaddedShapeFn(const Tensor& tensor, xla::Shape* shape) {
  const tensorflow::XlaTensor* xla_tensor =
      tensorflow::XlaTensor::FromTensor(&tensor);
  if (xla_tensor == nullptr) {

but 1000 = 1k; I didn't choose the capitalization.)\n\n<decimalExponent> ::= \"e\" <signedNumber> | \"E\" <signedNumber> ```\n\nNo matter which of the three exponent forms is used, no quantity may represent a number greater than 2^63-1 in magnitude, nor may it have more than 3 decimal places. Numbers larger or more precise will be capped or rounded up. (E.g.: 0.1m will rounded up to 1m.) This may be extended in the future if we require larger or smaller quantities.\n\nWhen a Quantity is parsed from...

	< go/types;

	# The vast majority of standard library packages should not be resorting to regexp.
	# go/types is a good chokepoint. It shouldn't use regexp, nor should anything
	# that is low-enough level to be used by go/types.
	regexp !< go/types;

	go/doc/comment, go/parser, internal/lazyregexp, text/template
	< go/doc;

      SourceOp op, Operation* const_op, int input_index,
      const operator_property::TensorProperty& tensor_property,
      PatternRewriter& rewriter) const {
    // Non-float tensors are neither weights nor require quantization.
    auto type = mlir::dyn_cast<ShapedType>(const_op->getResult(0).getType());
    if (!type || !mlir::isa<FloatType>(type.getElementType())) return success();

    DenseFPElementsAttr attr;