input-group-text input[type=checkbox],.input-group-text input[type=radio]{margin-top:0}.input-group-lg>.custom-select,.input-group-lg>.form-control:not(textarea){height:calc(1.5em + 1rem + 2px)}.input-group-lg>.custom-select,.input-group-lg>.form-control,.input-group-lg>.input-group-append>.btn,.input-group-lg>.input-group-append>.input-group-text,.input-group-lg>.input-group-prepend>.btn,.input-group-lg>.input-group-prepend>.input-group-text{padding:.5rem 1rem;font-size:1.25rem;line-height:1.5;...

input-group-text input[type=checkbox],.input-group-text input[type=radio]{margin-top:0}.input-group-lg>.custom-select,.input-group-lg>.form-control:not(textarea){height:calc(1.5em + 1rem + 2px)}.input-group-lg>.custom-select,.input-group-lg>.form-control,.input-group-lg>.input-group-append>.btn,.input-group-lg>.input-group-append>.input-group-text,.input-group-lg>.input-group-prepend>.btn,.input-group-lg>.input-group-prepend>.input-group-text{padding:.5rem 1rem;font-size:1.25rem;line-height:1.5;...

  Value reshaped_input = builder.create<TF::ReshapeOp>(
      RankedTensorType::get(shape, element_type), input, shape_cst);
  return reshaped_input;
}

// Create a tensor which is equal to input[begin: begin + size].
Value BuildSliceOp(ImplicitLocOpBuilder& builder,
                   ConversionPatternRewriter& rewriter, Value input,
                   Value begin, ArrayRef<int64_t> shape, Type idx_type,

  func.return %24 : tensor<1x4xf32>
}

// -----

// Coefficient inputs of LSTM op don't match the dimension with input operand `input_to_output_weights`.

    func.return %0 : tensor<*xf32>
  }

  // Verifies handling of cases involving multiple iteration of feeding inputs.
  // CHECK-LABEL: @const_input_required
  func.func @const_input_required(%arg0: tensor<10xf64>) -> tensor<?xf64> attributes {tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2,_arg3", outputs = "_retval0"}} {
    %cst = "tf.Const"() {value = dense<6> : tensor<1xi32>} : () -> tensor<1xi32>

		{
			name:    "wrong format",
			input:   "0: kubelet 65536",
			wantErr: true,
		},
		{
			name:    "non numeric 1",
			input:   "0: kubelet Foo 65536",
			wantErr: true,
		},
		{
			name:    "non numeric 2",
			input:   "0: kubelet 0 Bar",
			wantErr: true,
		},
		{
			name:    "overflow 1",
			input:   "0: kubelet 4294967296 2147483648",
			wantErr: true,
		},
		{

			},
			errors: map[string]string{
				"duration('1')":                      "compilation failed: ERROR: <input>:1:10: invalid duration argument",
				"duration('1d')":                     "compilation failed: ERROR: <input>:1:10: invalid duration argument",
				"duration('1us') < duration('1nns')": "compilation failed: ERROR: <input>:1:28: invalid duration argument",
			},
		},
		{name: "date format",

		if len(in) < n {
			panic("reflect: CallSlice with too few input arguments")
		}
		if len(in) > n {
			panic("reflect: CallSlice with too many input arguments")
		}
	} else {
		if isVariadic {
			n--
		}
		if len(in) < n {
			panic("reflect: Call with too few input arguments")
		}
		if !isVariadic && len(in) > n {
			panic("reflect: Call with too many input arguments")
		}
	}
	for _, x := range in {

	cases := []struct {
		input    string
		expected error
	}{
		{
			input:    "foo",
			expected: nil,
		},
		{
			input:    "%HOSTNAME%",
			expected: nil,
		},
		{
			input:    "100%%",
			expected: nil,
		},
		{
			input:    "prefix %HOSTNAME% suffix",
			expected: nil,
		},
		{

		// Either already satisfied or worker count changed while we waited for the lock.
		return
	}
	for len(p.workers) < n {
		input := make(chan ReplicationWorkerOperation, 10000)
		p.workers = append(p.workers, input)

		go p.AddWorker(input, &p.activeWorkers)
	}
	for len(p.workers) > n {
		worker := p.workers[len(p.workers)-1]
		p.workers = p.workers[:len(p.workers)-1]