// CHECK:         }, {
    // CHECK:           "tf_device.cluster"
    // CHECK:             %[[A:.+]] = "tf.OpA"
    // CHECK:             %[[A_SHARD:.+]] = "tf.XlaSpmdFullToShardShape"(%[[A]]) <{dim = -1 : i64, manual_sharding = "\08\03\1A\02\02\01\22\02\00\01", unspecified_dims = []}> : (tensor<2x2xi64>) -> tensor<1x2xi64>
    // CHECK:             %[[B:.+]] = "tf._XlaHostComputeMlir"(%[[A_SHARD]])

    }
    if (c->Rank(s0) != c->Rank(s1)) {
      return false;
    }
    for (int i = 0; i < c->Rank(s0); ++i) {
      if (!c->Dim(s0, i).SameHandle(c->Dim(s1, i))) {
        int64_t val0 = c->Value(c->Dim(s0, i));
        int64_t val1 = c->Value(c->Dim(s1, i));
        // Negative value is treated as unknown so all negative values indicate
        // the same dimension.

// CHECK-LABEL: dot_general_upstream_srq_too_many_contractions
// CHECK: stablehlo.dot_general
// CHECK-NOT: tfl.batch_matmul

// -----

// Tests static range quantized dot_general with unsupported contracting dim

func.func @dot_general_upstream_srq_wrong_contracting(%arg0: tensor<1x2x3x4x!quant.uniform<i8:f32, 1.000000e+0>>) -> tensor<1x4x3x5x!quant.uniform<i8:f32, 4.000000e+0>> {

on(t){var e=h.props[t],i=e.diff,n=e.bgPos,r=e.steps,o="bgy"===t?"height":"width",s=l[o]-u[o];if(s<i)u[o]=l[o]+i-s;else if(i<s){var a=u[o]/wi(n,o,h.$el);a&&(h.props[t].steps=r.map(function(t){return t-(s-i)/a}))}l=rt.cover(c,u)}),t.dim=l}}},write:function(t){var e=t.dim;t.active?e&&Ve(this.$el,{backgroundSize:e.width+"px "+e.height+"px",backgroundRepeat:"no-repeat"}):Ve(this.$el,{backgroundSize:"",backgroundRepeat:""})},events:["resize"]},methods:{reset:function(){var i=this;J(this.getCss(0),function(t,e){return...

            "format": "int32",
            "type": "integer"
          },
          "insecureSkipTLSVerify": {

40.809379577636719 1 1.20 7 0 0.5 0 0 7.5 2 1 166 42 green 0.00 0.0 0.0 54 40 8.05 1312 209.01 1 Manhattan 020901 1020901 E MN09 Morningside Heights 3802 1647 224 1 Manhattan 022400 1022400 E MN03 Central Harlem North-Polo Grounds 3803 nyc-taxi-data-100k-single-delim.csv trip_id,vendor_id,pickup_datetime,dropoff_datetime,store_and_fwd_flag,rate_code_id,pickup_longitude,pickup_latitude,dropoff_longitude,dropoff_latitude,passenger_count,trip_distance,fare_amount,extra,mta_tax,tip_amount,tolls_amount,ehail_fee,im...

    //     %0 = stablehlo.constant dense<0> : tensor<i32>
    //     %1 = stablehlo.get_dimension_size %arg1, dim = 0 : (tensor<?x1024xf32>) -> tensor<i32>
    //     %2 = stablehlo.compare  EQ, %0, %1 : (tensor<i32>, tensor<i32>) -> tensor<i1>

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