// CHECK: %[[ARG0_CAST:.*]] = "tf.Cast"(%[[ARG0]]
// CHECK: %[[ARG0_REDUCE:.*]] = "tf.Einsum"(%[[ARG0_CAST]]
// CHECK-SAME: __tf_quant_created_einsum
// CHECK: %[[ARG0_ZP:.*]] = "tf.Mul"(%[[ARG0_REDUCE]]

// CHECK: %[[ARG1_CAST:.*]] = "tf.Cast"(%[[ARG1]]
// CHECK: %[[ARG1_REDUCE:.*]] = "tf.Einsum"({{.*}}, %[[ARG1_CAST]]
// CHECK-SAME: __tf_quant_created_einsum
// CHECK: %[[ARG1_ZP:.*]] = "tf.Mul"(%[[ARG1_REDUCE]]

            tensor_spec.TensorSpec(
                shape=input_shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
    )
    return model

  # Prepares sample einsum input data shapes.
  # This function returns:
  # 1. Shape for input 1
  # 2. Shape for input 2
  # 3. Shape for bias
  # 4. Signature for input 1 (Could contain None dimension)

          quantize_func_count++;
        } else if (func_name.starts_with("dequantize_i")) {
          dequantize_func_count++;
        }
      } else if (auto einsum = llvm::isa<TF::EinsumOp>(op)) {
        if (IsInCompsiteFunction(op)) return;
        // Leftover Einsum ops are always non-quantized.
        auto op_name = op->getName().stripDialect();
        func_count_map[op_name].num_float++;
      }
    });

TensorFlow graph export by making transformation such as replacing or
removing MLIR or XLA specific attributes that are not legal in TensorFlow
graph.
### `-tf-batch-matmul-to-tf-einsum`

_Replace TF BatchMatMul op by TF Einsum op._

### `-tf-broadcast-fold`

_Fold explicit broadcasts into the following operations if they support implicit broadcasting on their operand._

type IfaceAny struct {
	T1  T1
	Any any `xml:",any"`
	T2  T2
}

type Generic[T any] struct {
	X T
}

var (
	nameAttr     = "Sarah"
	ageAttr      = uint(12)
	contentsAttr = "lorem ipsum"
	empty        = ""
)

// Unless explicitly stated as such (or *Plain), all of the
// tests below are two-way tests. When introducing new tests,
// please try to make them two-way as well to ensure that

This is why working with archives is similar to working with files and directories.

Out of the box, Gradle supports the creation of ZIP and TAR archives and, by extension, Java's JAR, WAR, and EAR formats—Java's archive formats are all ZIPs.

#include "tensorflow/compiler/mlir/lite/utils/utils.h"
#include "tensorflow/compiler/mlir/lite/utils/validators.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/einsum.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/unroll_batch_matmul.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/dynamic_shape_utils.h"

      "Dequantize",
      "Diag",
      "DynamicInfeedEnqueueTupleOp",
      "DynamicInfeedDequeueTupleOp",
      "DynamicStitch",
      "DynamicPartition",
      "Einsum",
      "EmptyTensorList",
      "EnsureShape",
      "ExtractImagePatches",
      "Igamma",
      "IgammaGradA",
      "RandomGammaGrad",
      "Igammac",
      "FFT",
      "FFT2D",

d,rebil-a-si,?f-morf,i&-morf,am&-sndnyd,detsohpw,??l&ecelffaw,uf-ytnuob:.a&hpla,teb,?,?ppmswa,ru-&elpmis,taen,?ssukoreh,xegap,?m&n-morf,pml.ppa,rofe&pyt.orp,rerac-htlaeh,?sacrasevres,uirarret-yltsaf,?n&a&cilbuper-a-si,f&-sllub-a-si,racsan-a-si,?i&cisum-a-si,ratrebil-a-si,?tarukas,?c,dc&hsums,umpw,xirtrepmi,?eerg-a-si,i&-morf,jod,?m-morf,o&ehtnaptog,isam-al-a-tse,r&italik,tap-el-tse,?s&iam-al-a-tse,replausunu,??pj,t-morf,?o&bordym,c,hce-namtsop,jodsnd,m&-morf,ed-baltlow,?n:iloxip,,t&ingocnozama.&...

d,rebil-a-si,?f-morf,i&-morf,am&-sndnyd,detsohpw,??l&ecelffaw,uf-ytnuob:.a&hpla,teb,?,?ppmswa,ru-&elpmis,taen,?ssukoreh,xegap,?m&n-morf,pml.ppa,rofe&pyt.orp,rerac-htlaeh,?sacrasevres,uirarret-yltsaf,?n&a&cilbuper-a-si,f&-sllub-a-si,racsan-a-si,?i&cisum-a-si,ratrebil-a-si,?tarukas,?c,dc&hsums,umpw,xirtrepmi,?eerg-a-si,i&-morf,jod,?m-morf,o&ehtnaptog,isam-al-a-tse,r&italik,tap-el-tse,?s&iam-al-a-tse,replausunu,??pj,t-morf,?o&bordym,c,hce-namtsop,jodsnd,m&-morf,ed-baltlow,?n:iloxip,,t&ingocnozama.&...