// CHECK-NEXT: %[[CONV2D_0:.*]] = "tf.Conv2D"(%arg0, %arg1)
// CHECK-SAME: data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true
// CHECK-SAME: attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations"
// CHECK-NEXT: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%[[CONV2D_0]], %arg2)
// CHECK-NEXT: %[[RELU6_0:.*]] = "tf.Relu6"(%[[BIASADD_0]])

!important;\n}\n\n.pl-1,\n.px-1 {\n  padding-left: 0.25rem !important;\n}\n\n.p-2 {\n  padding: 0.5rem !important;\n}\n\n.pt-2,\n.py-2 {\n  padding-top: 0.5rem !important;\n}\n\n.pr-2,\n.px-2 {\n  padding-right: 0.5rem !important;\n}\n\n.pb-2,\n.py-2 {\n  padding-bottom: 0.5rem !important;\n}\n\n.pl-2,\n.px-2 {\n  padding-left: 0.5rem !important;\n}\n\n.p-3 {\n  padding: 1rem !important;\n}\n\n.pt-3,\n.py-3 {\n  padding-top: 1rem !important;\n}\n\n.pr-3,\n.px-3 {\n  padding-right: 1rem !important;\n}\n\n.pb-3,\n.py-3...

/* Override some defaults */

html {
	min-height: 100%;
	position: relative;
}

body {
	padding: 1em 0;
	margin: 56px 0 4em;
}

footer {
	width: 100%;
	bottom: 0;
	height: 4em;
	position: absolute;
	border-top: 1px solid;
	padding-top: 1em;
}

h1,
h2,
h3 {
	line-height: 32px;
}

h1 {
	font-size: 30px;
}

h2 {
	font-size: 24px;
}

h3 {
	font-size: 18px;
}

	tests := []struct {
		noun    string
		padding string
		want    string
	}{
		{
			noun:    "Frog",
			padding: " ",
			want:    " a ",
		},
		{
			noun:    "frogs",
			padding: " ",
			want:    " ",
		},
		{
			noun:    "apple",
			padding: "",
			want:    "an",
		},
		{
			noun:    "Apples",
			padding: " ",
			want:    " ",
		},
		{
			noun:    "Ingress",

  %0 = "tfl.conv_2d"(%arg0, %arg1, %arg2) {dilation_h_factor = 2 : i32, dilation_w_factor = 2 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>,...

  // CHECK: %[[VAL_1:.*]] = "tf.Identity"(%[[VAL_0]]) : (tensor<*xf32>) -> tensor<*xf32>...

// Returns true if the provided padding in the composite op can *not* be 
// satisfied by SAME or VALID tensorflow padding.
def HasCustomPadding:
  Constraint<CPred<"GetAvgPoolOpPadAttr($_builder, (*$0.begin()).getDefiningOp<mhlo::CompositeOp>()) == $_builder.getStringAttr(\"CUSTOM\")">>;

// Returns true if the provided padding in the composite op can be satisfied 
// by SAME or VALID tensorflow padding.

      // stablehlo.convolution op needs explicit padding to be set to model any
      // Transposed-Convolution in JAX/PT. Checking to see if-
      // 1. Pre set padding matches to the desired padding
      // 2. Output size respects the `VALID` padding scenario
      if ((padding[2 * i] == padding[2 * i + 1]) &&
          (((kernel_size - 1) != padding[2 * i]) ||

    SmallVector<int64_t> padded_shape(tensor_shape.begin(), tensor_shape.end());
    for (int i = 0; i < padded_shape.size(); ++i) {
      // Left padding + right padding.
      int32_t padding = tfl_pad_values[i * 2] + tfl_pad_values[i * 2 + 1];
      padded_shape[i] += padding;
    }

    return padded_shape;
  }

  std::pair<int64_t, int64_t> GetDimSize(

#include "mlir/IR/Builders.h"  // from @llvm-project

namespace mlir::quant {

// Caclulate padding values for XLA ops.
// Padding values for Uniform Quantized ops can be generated with this method as
// well as it shares the same definition for padding attribute with the XLA ops.
Value CalculatePaddingAndPadIfNeeded(OpBuilder &builder, Location loc,